Maxwell Ratings: Region by region look at 412 teams (Nov. 13)

Here are the Maxwell Ratings through the regular season.

CL=Team’s ranking in its classification. OV=Team’s ranking in all classifications. RAT=Team rating. SOS=Strength of schedule.

CL OV 1 – AAAAAA WL RAT SOS
4 7 Camden County 9 – 1 88.47 70.22
5 8 Colquitt County 8 – 2 86.70 72.12
14 25 Lowndes 7 – 3 77.17 63.62
17 31 Valdosta 6 – 4 75.64 69.78
21 38 Tift County 5 – 5 72.41 71.28
22 41 Coffee 5 – 5 71.93 71.27
35 78 Brunswick 3 – 7 62.41 70.39
CL OV 2 – AAAAAA WL RAT SOS
10 17 Lovejoy 9 – 1 80.30 55.92
43 108 Alcovy 7 – 3 55.65 44.38
44 112 Newton 5 – 5 55.27 58.37
48 129 Rockdale County 6 – 4 52.42 50.14
52 139 Luella 5 – 5 50.81 51.31
60 264 Druid Hills 4 – 6 31.29 39.99
61 286 Morrow 1 – 9 26.45 51.47
CL OV 3 – AAAAAA WL RAT SOS
6 12 Langston Hughes 9 – 1 83.60 59.39
26 60 East Coweta 5 – 5 66.20 67.71
31 70 Newnan 6 – 4 63.81 62.51
33 75 Westlake 5 – 5 62.79 62.99
45 120 Douglas County 3 – 7 53.77 62.01
46 124 Tri-Cities 1 – 8 – 1 53.08 64.67
CL OV 4 – AAAAAA WL RAT SOS
7 13 Hillgrove 9 – 1 83.25 62.69
9 16 McEachern 8 – 2 80.52 63.54
12 20 North Cobb 8 – 2 79.91 61.35
15 29 Marietta 8 – 2 76.50 59.43
28 66 Harrison 5 – 5 64.01 65.23
49 130 Campbell 4 – 6 52.33 58.00
51 137 South Cobb 3 – 7 51.56 64.04
56 173 Pebblebrook 3 – 7 44.83 59.69
59 209 Kennesaw Mountain 2 – 8 38.46 60.07
CL OV 5 – AAAAAA WL RAT SOS
18 32 Walton 6 – 4 75.52 70.87
19 33 Milton 8 – 2 75.49 63.23
20 34 Lassiter 7 – 3 75.19 67.14
23 45 Etowah 7 – 3 69.27 62.82
30 69 Cherokee 5 – 5 63.90 64.51
37 84 Woodstock 3 – 7 60.66 66.68
39 90 Roswell 4 – 6 59.90 63.92
40 93 Wheeler 3 – 7 59.43 65.06
CL OV 6 – AAAAAA WL RAT SOS
16 30 Alpharetta 8 – 2 75.87 61.99
24 47 West Forsyth 7 – 3 68.89 60.60
27 65 Lambert 6 – 4 64.56 59.06
34 76 North Forsyth 7 – 3 62.77 55.18
38 89 South Forsyth 7 – 3 60.18 52.98
47 128 Chattahoochee 4 – 6 52.64 58.22
54 155 Centennial 1 – 9 48.49 63.57
57 200 Johns Creek 0 – 10 39.61 62.91
CL OV 7 – AAAAAA WL RAT SOS
1 2 North Gwinnett 9 – 1 94.72 66.44
2 5 Collins Hill 9 – 1 93.10 60.57
3 6 Norcross 8 – 2 92.92 59.97
8 15 Peachtree Ridge 7 – 3 81.99 63.60
11 19 Mill Creek 6 – 4 79.91 64.61
55 172 Duluth 3 – 7 45.17 67.12
58 206 Mountain View 2 – 8 38.97 70.92
62 316 Meadowcreek 1 – 9 20.72 66.89
63 341 Habersham Central 0 – 10 14.37 67.69
CL OV 8 – AAAAAA WL RAT SOS
13 21 Archer 10 – 0 79.74 57.97
25 49 Dacula 6 – 4 68.58 64.36
29 68 Brookwood 5 – 5 63.90 64.53
32 72 Grayson 4 – 6 63.27 65.77
36 82 South Gwinnett 5 – 4 60.99 59.03
41 99 Parkview 3 – 7 57.30 66.14
42 107 Shiloh 5 – 5 55.70 56.04
50 133 Central Gwinnett 2 – 8 52.23 64.26
53 145 Berkmar 3 – 7 49.62 58.19
CL OV 1 – AAAAA WL RAT SOS
5 27 Thomas County Central 8 – 2 76.98 60.37
8 39 Lee County 9 – 1 72.16 39.79
9 44 Harris County 8 – 2 69.67 57.86
29 125 Bainbridge 5 – 5 53.08 56.43
55 258 Hardaway 2 – 8 32.12 55.08
57 261 Northside (Columbus) 2 – 8 31.86 52.27
CL OV 2 – AAAAA WL RAT SOS
4 23 Northside (Warner Robins) 9 – 1 78.75 56.06
6 28 Warner Robins 9 – 1 76.89 54.07
10 50 Houston County 7 – 3 68.51 57.17
17 86 Lakeside (Evans) 6 – 4 60.40 54.55
23 113 Evans 5 – 5 55.05 55.32
27 121 Jones County 5 – 5 53.76 54.26
53 252 Grovetown 2 – 8 33.27 61.67
70 365 Greenbrier 0 – 10 9.25 56.83
CL OV 3 – AAAAA WL RAT SOS
13 63 Ware County 9 – 1 65.40 38.55
26 119 Richmond Hill 9 – 1 53.97 25.92
39 160 Glynn Academy 6 – 4 47.42 39.77
48 208 Effingham County 6 – 4 38.65 36.17
60 266 Bradwell Institute 4 – 6 30.73 35.95
62 281 Jenkins 3 – 6 – 1 27.44 32.48
71 366 Groves 1 – 9 9.24 39.96
75 395 Windsor Forest 0 – 10 -5.60 41.26
CL OV 4-Div A – AAAAA WL RAT SOS
21 101 Mundy’s Mill 6 – 4 56.90 48.82
41 165 Union Grove 6 – 4 46.64 42.09
44 188 Drew 4 – 6 42.47 45.06
56 259 Ola 2 – 7 31.98 45.19
66 297 Forest Park 2 – 8 25.05 46.58
73 378 Mount Zion (Jonesboro) 0 – 10 4.49 42.10
CL OV 4-Div B – AAAAA WL RAT SOS
2 14 Creekside 9 – 0 83.13 53.73
15 71 McIntosh 8 – 2 63.56 50.17
16 80 Whitewater 7 – 3 61.99 51.95
34 144 Starr’s Mill 4 – 6 49.63 53.52
36 149 Northgate 4 – 6 48.99 54.51
CL OV 5 – AAAAA WL RAT SOS
12 56 North Paulding 9 – 1 67.05 44.31
14 64 South Paulding 9 – 1 64.81 44.86
20 100 Allatoona 7 – 3 57.02 45.16
22 106 East Paulding 7 – 3 55.87 45.56
33 142 New Manchester 6 – 4 50.02 45.97
38 158 Rome 5 – 5 47.46 46.39
43 179 Villa Rica 5 – 5 43.79 46.79
47 207 Woodland (Cartersville) 4 – 6 38.79 47.60
51 237 Paulding County 2 – 8 34.99 47.95
61 276 Hiram 1 – 9 28.76 48.55
69 326 Lithia Springs 0 – 10 18.20 48.74
CL OV 6 – AAAAA WL RAT SOS
7 37 Tucker 10 – 0 73.86 41.13
19 96 Stephenson 8 – 2 59.03 38.71
25 116 Mays 7 – 3 54.69 41.39
30 134 M.L. King 7 – 3 52.20 39.68
35 147 Arabia Mountain 7 – 3 49.33 37.10
50 235 Miller Grove 4 – 6 35.17 41.95
59 265 Dunwoody 3 – 7 31.19 42.68
63 285 Lakeside (Atlanta) 2 – 8 26.81 43.16
64 294 Southwest Dekalb 2 – 8 25.78 43.75
72 376 Clarkston 4 – 5 – 1 6.70 10.71
74 383 North Atlanta 0 – 10 3.12 41.82
CL OV 7-Div A – AAAAA WL RAT SOS
24 114 Sequoyah 8 – 2 55.01 39.98
28 123 Creekview 8 – 2 53.57 39.69
37 153 Northview 5 – 5 48.78 47.37
49 215 Cambridge 3 – 7 37.90 46.62
58 262 Forsyth Central 2 – 8 31.68 44.91
68 320 North Springs 1 – 9 19.43 47.77
CL OV 7-Div B – AAAAA WL RAT SOS
3 22 Kell 10 – 0 79.38 44.90
31 135 Riverwood 7 – 3 52.04 41.23
45 199 Pope 3 – 7 39.97 53.71
54 253 Sprayberry 2 – 8 33.07 50.58
65 295 Osborne 2 – 8 25.61 42.48
CL OV 8 – AAAAA WL RAT SOS
1 11 Gainesville 9 – 1 85.12 51.91
11 54 Flowery Branch 7 – 3 67.76 52.22
18 87 Clarke Central 7 – 3 60.37 50.58
32 138 Heritage (Conyers) 6 – 4 51.42 49.60
40 161 Loganville 5 – 5 47.23 49.35
42 178 Cedar Shoals 3 – 7 44.25 53.47
46 202 Salem 3 – 7 39.52 48.30
52 251 Winder-Barrow 2 – 8 33.38 51.77
67 301 Apalachee 0 – 10 24.09 55.32
CL OV 1 – AAAA WL RAT SOS
16 103 Westover 8 – 2 56.55 42.56
20 117 Cairo 7 – 3 54.35 43.33
31 181 Monroe 6 – 4 43.49 39.43
32 182 Crisp County 6 – 4 43.36 40.45
51 231 Americus-Sumter 4 – 6 35.47 38.60
53 239 Worth County 3 – 7 34.75 41.72
62 307 Albany 2 – 8 22.59 39.15
71 352 Dougherty 0 – 10 13.03 46.46
CL OV 2 – AAAA WL RAT SOS
11 79 Veterans 7 – 3 62.09 50.13
15 98 Mary Persons 9 – 1 57.78 38.04
21 122 Westside (Macon) 7 – 3 53.57 38.20
39 198 Rutland 6 – 4 40.09 33.58
40 204 West Laurens 3 – 7 39.14 46.07
41 205 Baldwin 3 – 7 38.97 47.14
52 236 Perry 3 – 7 35.17 44.82
70 351 Howard 2 – 8 13.36 36.68
CL OV 3-Div A – AAAA WL RAT SOS
6 46 Statesboro 9 – 1 68.98 43.01
8 57 Wayne County 8 – 2 66.82 52.78
19 115 Liberty County 8 – 2 54.85 37.93
48 228 South Effingham 5 – 5 35.67 35.53
CL OV 3-Div B – AAAA WL RAT SOS
7 55 Burke County 7 – 2 67.12 45.17
35 192 Richmond Academy 5 – 5 41.52 41.61
42 213 Glenn Hills 5 – 5 37.98 36.78
67 337 Hephzibah 3 – 7 15.71 30.31
69 345 Butler 1 – 9 13.97 42.89
74 361 Cross Creek 2 – 8 9.79 31.78
CL OV 4-Div A – AAAA WL RAT SOS
9 61 Stockbridge 9 – 1 65.76 43.41
13 92 Eagle’s Landing 8 – 2 59.44 41.97
36 193 Locust Grove 6 – 4 41.45 38.38
44 217 Dutchtown 4 – 6 37.76 42.20
49 229 Woodland (Stockbridge) 2 – 8 35.67 55.12
CL OV 4-Div B – AAAA WL RAT SOS
3 18 Griffin 10 – 0 79.92 37.19
30 175 Jonesboro 6 – 4 44.54 38.19
46 219 Riverdale 4 – 6 37.42 44.15
54 240 Upson-Lee 4 – 6 34.57 42.79
64 315 North Clayton 1 – 9 21.15 44.70
65 318 Spalding 1 – 9 19.57 39.86
CL OV 5 – AAAA WL RAT SOS
1 4 Sandy Creek 9 – 0 – 1 93.27 54.25
2 10 Carrollton 9 – 1 85.58 50.18
10 77 Troup 6 – 4 62.68 53.16
14 95 Alexander 6 – 4 59.19 52.18
26 157 LaGrange 3 – 7 48.17 58.22
37 194 Shaw 4 – 6 41.44 51.78
47 227 Fayette County 1 – 9 36.15 60.02
60 296 Columbus 1 – 9 25.34 48.56
CL OV 6-Div A – AAAA WL RAT SOS
5 42 Marist 8 – 2 71.77 44.15
24 150 Columbia 6 – 4 48.96 43.50
38 197 Chamblee 6 – 4 40.18 35.94
50 230 Lithonia 4 – 6 35.59 38.48
58 277 Stone Mountain 4 – 6 28.28 34.13
68 343 Redan 1 – 9 14.17 46.48
CL OV 6-Div B – AAAA WL RAT SOS
22 127 Carver (Atlanta) 6 – 3 52.72 46.23
25 151 Grady 8 – 2 48.95 34.79
28 164 Banneker 5 – 3 – 1 46.75 39.75
45 218 Washington 6 – 3 37.45 23.15
75 371 Therrell 1 – 9 7.92 40.72
77 394 South Atlanta 0 – 10 -4.41 35.42
CL OV 7-Div A – AAAA WL RAT SOS
27 159 Gilmer 8 – 2 47.42 31.92
29 166 Cedartown 9 – 1 46.61 28.05
43 214 Pickens 6 – 4 37.95 32.13
55 246 Cass 4 – 6 34.12 39.50
63 309 River Ridge 2 – 8 22.06 38.77
CL OV 7-Div B – AAAA WL RAT SOS
17 104 Dalton 8 – 2 56.36 37.19
23 141 Northwest Whitfield 9 – 1 50.27 28.01
59 282 Ridgeland 3 – 7 27.32 37.72
61 306 Southeast Whitfield 4 – 6 23.04 30.46
73 357 LaFayette 2 – 8 11.46 31.65
76 375 Heritage (Ringgold) 1 – 9 6.91 31.00
CL OV 8 – AAAA WL RAT SOS
4 36 Monroe Area 10 – 0 74.69 38.85
12 91 Chestatee 8 – 2 59.83 42.74
18 110 Lanier 8 – 2 55.34 34.41
33 183 Eastside 5 – 5 43.26 44.57
34 191 Stephens County 5 – 5 41.64 40.68
56 255 Madison County 4 – 6 32.87 41.96
57 257 Lumpkin County 2 – 8 32.56 49.26
66 328 Walnut Grove 1 – 9 17.99 43.09
72 355 Johnson (Gainesville) 2 – 8 12.12 32.30
CL OV 1 – AAA WL RAT SOS
17 132 Appling County 8 – 2 52.27 30.77
21 156 Pierce County 9 – 1 48.33 28.26
33 254 Southeast Bulloch 7 – 2 – 1 32.96 22.39
35 268 Johnson (Savannah) 4 – 6 29.94 39.12
39 303 Tattnall County 4 – 6 23.60 29.62
45 327 Brantley County 3 – 7 17.99 27.96
51 342 Savannah 1 – 9 14.29 36.98
52 344 Beach 1 – 9 14.09 36.29
CL OV 2 – AAA WL RAT SOS
12 85 Carver (Columbus) 9 – 1 60.43 31.93
24 185 Peach County 7 – 3 42.91 31.49
34 256 Pike County 5 – 5 32.66 33.00
40 305 Henry County 3 – 7 23.06 39.86
47 332 Jackson 2 – 8 17.55 35.25
54 356 Central (Macon) 1 – 9 11.77 35.13
57 388 Southwest 0 – 10 0.29 31.00
CL OV 3 – AAA WL RAT SOS
2 3 Washington County 9 – 0 94.22 42.60
20 148 Jefferson County 7 – 3 49.23 40.73
22 163 Thomson 3 – 6 46.96 58.09
29 216 Dodge County 4 – 6 37.86 42.45
37 275 Swainsboro 2 – 8 29.08 44.97
CL OV 4 – AAA WL RAT SOS
7 53 Callaway 9 – 1 67.84 40.46
19 146 Central (Carrollton) 8 – 2 49.61 34.25
26 196 B.E.S.T. Academy 6 – 4 40.25 29.62
27 211 Chapel Hill 5 – 5 38.38 38.62
36 269 Maynard Jackson 5 – 5 29.79 28.44
38 289 Rockmart 3 – 7 26.16 39.42
50 340 Haralson County 2 – 8 14.55 34.87
53 349 Douglass 1 – 9 13.66 36.64
CL OV 5 – AAA WL RAT SOS
6 51 Cartersville 10 – 0 68.37 22.45
18 140 Ringgold 8 – 2 50.66 21.90
32 249 Adairsville 7 – 3 33.87 18.22
43 317 Sonoraville 4 – 6 20.59 29.50
44 325 North Murray 4 – 6 18.42 21.27
56 381 Lakeview-Ft. Oglethorpe 3 – 7 4.03 20.74
58 393 Coahulla Creek 2 – 8 -4.17 20.15
59 404 Gordon Central 0 – 10 -18.53 21.79
CL OV 6 – AAA WL RAT SOS
3 40 Woodward Academy 8 – 2 72.01 53.29
4 43 Blessed Trinity 7 – 3 70.25 60.65
5 48 St. Pius X 7 – 3 68.61 57.30
13 94 Cedar Grove 6 – 4 59.27 49.89
15 118 Decatur 7 – 3 54.32 40.66
28 212 Towers 5 – 5 38.08 40.49
46 329 McNair 1 – 9 17.82 50.37
60 407 Cross Keys 1 – 9 -24.09 5.36
CL OV 7 – AAA WL RAT SOS
1 1 Buford 10 – 0 101.65 47.44
8 62 North Hall 8 – 2 65.64 45.90
10 73 White County 8 – 2 63.26 43.17
16 126 West Hall 6 – 4 52.84 43.76
23 174 Dawson County 5 – 5 44.73 46.81
25 195 East Hall 5 – 5 40.62 35.91
42 313 Fannin County 2 – 8 21.30 48.85
55 359 Banks County 0 – 10 10.15 44.43
CL OV 8 – AAA WL RAT SOS
9 67 Hart County 9 – 1 63.96 39.32
11 81 Elbert County 8 – 2 61.53 41.34
14 111 North Oconee 7 – 3 55.31 36.21
30 238 Morgan County 6 – 4 34.93 29.46
31 244 Jackson County 4 – 6 34.17 38.40
41 312 Oconee County 2 – 9 21.48 41.60
48 333 Franklin County 1 – 9 17.07 41.83
49 335 East Jackson 2 – 9 16.01 35.26
CL OV 1 – AA WL RAT SOS
7 74 Brooks County 9 – 1 62.84 44.07
13 109 Thomasville 7 – 3 55.44 46.21
18 167 Cook 6 – 4 46.23 41.69
19 169 Fitzgerald 5 – 4 – 1 45.98 44.31
20 180 Early County 5 – 5 43.50 41.80
30 245 Pelham 4 – 6 34.13 39.37
38 290 Berrien 3 – 7 26.03 35.79
CL OV 2 – AA WL RAT SOS
3 26 Benedictine 10 – 0 77.02 30.49
10 97 Vidalia 9 – 1 57.83 31.07
15 143 Bryan County 8 – 2 49.69 31.98
23 203 Metter 7 – 3 39.34 31.28
29 242 McIntosh County Academy 5 – 5 34.55 34.48
35 272 Bacon County 4 – 6 29.49 35.68
40 298 Toombs County 4 – 6 24.85 30.54
47 347 Long County 2 – 8 13.81 32.41
51 368 Jeff Davis 1 – 9 8.99 33.42
52 370 Atkinson County 1 – 9 7.93 31.89
CL OV 3 – AA WL RAT SOS
12 105 Laney 8 – 2 56.09 28.83
21 190 Dublin 7 – 3 41.86 29.65
31 247 Westside (Augusta) 7 – 3 34.09 20.41
37 288 Screven County 5 – 5 26.37 23.88
49 350 Harlem 6 – 4 13.63 9.82
55 382 East Laurens 1 – 9 3.70 28.29
59 396 Josey 0 – 10 -5.69 32.03
CL OV 4 – AA WL RAT SOS
4 35 Lamar County 10 – 0 74.83 31.00
16 152 Bleckley County 8 – 2 48.93 33.46
24 210 Macon County 6 – 4 38.43 35.09
27 232 Taylor County 6 – 4 35.36 27.79
28 241 Northeast 7 – 3 34.56 21.22
41 300 Putnam County 3 – 7 24.41 35.05
44 321 Monticello 3 – 7 19.17 30.29
62 409 Crawford County 0 – 10 -24.75 29.77
CL OV 5 – AA WL RAT SOS
8 83 Heard County 9 – 1 60.97 29.61
14 131 Bowdon 9 – 1 52.32 26.42
22 201 Kendrick 6 – 4 39.61 33.22
25 225 Manchester 7 – 3 36.86 22.81
26 226 Bremen 5 – 5 36.86 36.92
34 267 Spencer 5 – 5 30.60 32.65
46 339 Jordan 2 – 8 14.67 34.71
53 374 Temple 1 – 9 7.00 30.13
56 384 Chattahoochee County 2 – 8 2.74 27.65
CL OV 6 – AA WL RAT SOS
1 9 Lovett 9 – 1 86.34 66.21
2 24 Greater Atlanta Christian 9 – 1 78.04 33.36
9 88 Westminster 5 – 5 60.26 60.16
43 310 Wesleyan 2 – 8 21.89 39.70
54 379 Hapeville Charter 1 – 8 – 1 4.17 37.34
CL OV 7 – AA WL RAT SOS
5 58 Calhoun 9 – 1 66.41 27.16
32 248 Model 7 – 3 33.96 25.94
33 260 Dade County 7 – 3 31.86 18.27
36 287 Coosa 6 – 4 26.37 18.08
39 292 Pepperell 4 – 6 25.92 30.91
48 348 Chattooga 2 – 8 13.66 32.20
57 385 Armuchee 1 – 9 1.92 25.53
60 403 Murray County 1 – 9 -17.26 21.64
CL OV 8 – AA WL RAT SOS
6 59 Washington-Wilkes 10 – 0 66.26 15.75
11 102 Jefferson 8 – 2 56.80 27.68
17 162 Rabun County 8 – 2 47.11 17.62
42 304 Union County 6 – 4 23.51 18.13
45 338 Riverside Military Academy 5 – 5 14.92 16.69
50 367 Greene County 3 – 7 9.10 25.33
58 389 Oglethorpe County 1 – 9 -1.72 22.44
61 408 Social Circle 0 – 10 -24.61 20.99
CL OV 1 – A WL RAT SOS
2 136 Seminole County 10 – 0 51.91 20.88
25 273 Mitchell County 6 – 4 29.46 22.44
47 346 Miller County 4 – 6 13.92 21.57
51 360 Terrell County 5 – 5 10.03 10.77
53 363 Randolph-Clay 5 – 5 9.66 12.24
59 380 Calhoun County 2 – 8 4.11 20.41
69 401 Baconton Charter 2 – 8 -15.99 9.71
73 410 Stewart County 0 – 10 -32.56 16.73
CL OV 2 – A WL RAT SOS
4 168 Clinch County 7 – 3 46.02 36.43
8 177 Irwin County 8 – 1 – 1 44.36 26.74
16 223 Charlton County 6 – 4 37.08 32.16
27 278 Turner County 4 – 6 28.03 30.88
35 302 Wilcox County 3 – 7 23.89 31.47
41 323 Telfair County 4 – 6 18.65 23.45
54 364 Lanier County 2 – 8 9.30 24.79
CL OV 3-Div A – A WL RAT SOS
5 170 Calvary Day 8 – 2 45.80 23.18
32 291 Savannah Christian 5 – 4 – 1 25.98 23.26
34 299 Claxton 6 – 4 24.78 20.59
39 319 Savannah Country Day 5 – 5 19.53 19.05
44 331 Portal 3 – 6 – 1 17.66 24.63
64 392 Jenkins County 1 – 9 -3.92 20.71
CL OV 3-Div B – A WL RAT SOS
15 222 Johnson County 9 – 1 37.28 14.45
28 279 Emanuel County Institute 6 – 4 27.84 21.30
49 354 Treutlen 4 – 6 12.68 18.25
55 369 Wheeler County 3 – 7 8.25 20.26
58 377 Montgomery County 1 – 9 5.03 23.49
CL OV 4 – A WL RAT SOS
12 189 Marion County 9 – 1 42.00 19.77
13 220 Hawkinsville 7 – 3 37.37 28.14
17 224 Dooly County 7 – 3 36.95 25.36
24 271 Brookstone 6 – 4 29.65 24.83
31 284 Pacelli 7 – 3 26.97 4.66
56 372 Schley County 2 – 8 7.42 23.99
61 387 Greenville 1 – 9 1.29 30.03
71 405 Central (Talbotton) 0 – 10 -22.44 27.84
CL OV 5 – A WL RAT SOS
1 52 Eagle’s Landing Christian 9 – 0 68.37 25.93
7 176 Landmark Christian 8 – 2 44.36 31.38
14 221 Our Lady of Mercy 5 – 5 37.31 35.47
19 234 Holy Innocents 6 – 4 35.30 31.36
22 263 Pace Academy 5 – 5 31.61 27.88
62 390 Strong Rock Christian 3 – 7 -1.85 19.14
67 399 Mount Vernon Presbyterian 1 – 9 -11.49 30.70
CL OV 6-Div A – A WL RAT SOS
11 187 Darlington 8 – 2 42.71 25.38
21 250 Christian Heritage 8 – 2 33.53 14.56
29 280 Trion 5 – 4 27.66 23.11
48 353 Mount Zion (Carroll) 4 – 6 12.70 14.68
52 362 Gordon Lee 3 – 6 9.70 18.08
CL OV 6-Div B – A WL RAT SOS
3 154 Mount Pisgah Christian 10 – 0 48.76 22.53
9 184 Mount Paran Christian 8 – 2 43.08 29.20
36 308 King’s Ridge Christian 4 – 6 22.57 26.47
37 311 Whitefield Academy 4 – 6 21.70 26.27
38 314 Fellowship Christian 5 – 5 21.26 21.59
40 322 St. Francis 8 – 2 19.16 -3.20
46 336 Walker 4 – 5 15.89 21.37
74 411 North Cobb Christian 1 – 9 -37.02 -2.52
CL OV 7 – A WL RAT SOS
10 186 Aquinas 10 – 0 42.74 9.46
20 243 Lincoln County 7 – 3 34.54 16.33
30 283 First Presbyterian 6 – 4 27.28 20.09
42 324 Wilkinson County 4 – 6 18.46 22.47
50 358 Hancock Central 4 – 6 11.17 17.06
57 373 Georgia Military College 4 – 6 7.33 12.53
68 400 Twiggs County 2 – 8 -12.95 20.41
70 402 Glascock County 1 – 9 -16.33 11.80
72 406 Warren County 0 – 10 -22.77 21.17
CL OV 8 – A WL RAT SOS
6 171 Prince Avenue Christian 9 – 1 45.48 18.91
18 233 Commerce 7 – 3 35.32 27.47
23 270 Athens Christian 5 – 5 29.73 29.76
26 274 George Walton Academy 5 – 5 29.09 26.96
33 293 Athens Academy 6 – 4 25.91 22.54
43 330 Pinecrest Academy 5 – 5 17.77 17.32
45 334 Rabun Gap 6 – 4 16.88 -5.80
60 386 Towns County 4 – 6 1.40 12.85
63 391 Hebron Christian Academy 2 – 8 -3.47 20.06
65 397 Lakeview Academy 2 – 7 -6.98 8.51
CL OV 1 – AAAAAA WL RAT SOS
4 7 Camden County 9 – 1 88.47 70.22
5 8 Colquitt County 8 – 2 86.70 72.12
14 25 Lowndes 7 – 3 77.17 63.62
17 31 Valdosta 6 – 4 75.64 69.78
21 38 Tift County 5 – 5 72.41 71.28
22 41 Coffee 5 – 5 71.93 71.27
35 78 Brunswick 3 – 7 62.41 70.39
CL OV 2 – AAAAAA WL RAT SOS
10 17 Lovejoy 9 – 1 80.30 55.92
43 108 Alcovy 7 – 3 55.65 44.38
44 112 Newton 5 – 5 55.27 58.37
48 129 Rockdale County 6 – 4 52.42 50.14
52 139 Luella 5 – 5 50.81 51.31
60 264 Druid Hills 4 – 6 31.29 39.99
61 286 Morrow 1 – 9 26.45 51.47
CL OV 3 – AAAAAA WL RAT SOS
6 12 Langston Hughes 9 – 1 83.60 59.39
26 60 East Coweta 5 – 5 66.20 67.71
31 70 Newnan 6 – 4 63.81 62.51
33 75 Westlake 5 – 5 62.79 62.99
45 120 Douglas County 3 – 7 53.77 62.01
46 124 Tri-Cities 1 – 8 – 1 53.08 64.67
CL OV 4 – AAAAAA WL RAT SOS
7 13 Hillgrove 9 – 1 83.25 62.69
9 16 McEachern 8 – 2 80.52 63.54
12 20 North Cobb 8 – 2 79.91 61.35
15 29 Marietta 8 – 2 76.50 59.43
28 66 Harrison 5 – 5 64.01 65.23
49 130 Campbell 4 – 6 52.33 58.00
51 137 South Cobb 3 – 7 51.56 64.04
56 173 Pebblebrook 3 – 7 44.83 59.69
59 209 Kennesaw Mountain 2 – 8 38.46 60.07
CL OV 5 – AAAAAA WL RAT SOS
18 32 Walton 6 – 4 75.52 70.87
19 33 Milton 8 – 2 75.49 63.23
20 34 Lassiter 7 – 3 75.19 67.14
23 45 Etowah 7 – 3 69.27 62.82
30 69 Cherokee 5 – 5 63.90 64.51
37 84 Woodstock 3 – 7 60.66 66.68
39 90 Roswell 4 – 6 59.90 63.92
40 93 Wheeler 3 – 7 59.43 65.06
CL OV 6 – AAAAAA WL RAT SOS
16 30 Alpharetta 8 – 2 75.87 61.99
24 47 West Forsyth 7 – 3 68.89 60.60
27 65 Lambert 6 – 4 64.56 59.06
34 76 North Forsyth 7 – 3 62.77 55.18
38 89 South Forsyth 7 – 3 60.18 52.98
47 128 Chattahoochee 4 – 6 52.64 58.22
54 155 Centennial 1 – 9 48.49 63.57
57 200 Johns Creek 0 – 10 39.61 62.91
CL OV 7 – AAAAAA WL RAT SOS
1 2 North Gwinnett 9 – 1 94.72 66.44
2 5 Collins Hill 9 – 1 93.10 60.57
3 6 Norcross 8 – 2 92.92 59.97
8 15 Peachtree Ridge 7 – 3 81.99 63.60
11 19 Mill Creek 6 – 4 79.91 64.61
55 172 Duluth 3 – 7 45.17 67.12
58 206 Mountain View 2 – 8 38.97 70.92
62 316 Meadowcreek 1 – 9 20.72 66.89
63 341 Habersham Central 0 – 10 14.37 67.69
CL OV 8 – AAAAAA WL RAT SOS
13 21 Archer 10 – 0 79.74 57.97
25 49 Dacula 6 – 4 68.58 64.36
29 68 Brookwood 5 – 5 63.90 64.53
32 72 Grayson 4 – 6 63.27 65.77
36 82 South Gwinnett 5 – 4 60.99 59.03
41 99 Parkview 3 – 7 57.30 66.14
42 107 Shiloh 5 – 5 55.70 56.04
50 133 Central Gwinnett 2 – 8 52.23 64.26
53 145 Berkmar 3 – 7 49.62 58.19
CL OV 1 – AAAAA WL RAT SOS
5 27 Thomas County Central 8 – 2 76.98 60.37
8 39 Lee County 9 – 1 72.16 39.79
9 44 Harris County 8 – 2 69.67 57.86
29 125 Bainbridge 5 – 5 53.08 56.43
55 258 Hardaway 2 – 8 32.12 55.08
57 261 Northside (Columbus) 2 – 8 31.86 52.27
CL OV 2 – AAAAA WL RAT SOS
4 23 Northside (Warner Robins) 9 – 1 78.75 56.06
6 28 Warner Robins 9 – 1 76.89 54.07
10 50 Houston County 7 – 3 68.51 57.17
17 86 Lakeside (Evans) 6 – 4 60.40 54.55
23 113 Evans 5 – 5 55.05 55.32
27 121 Jones County 5 – 5 53.76 54.26
53 252 Grovetown 2 – 8 33.27 61.67
70 365 Greenbrier 0 – 10 9.25 56.83
CL OV 3 – AAAAA WL RAT SOS
13 63 Ware County 9 – 1 65.40 38.55
26 119 Richmond Hill 9 – 1 53.97 25.92
39 160 Glynn Academy 6 – 4 47.42 39.77
48 208 Effingham County 6 – 4 38.65 36.17
60 266 Bradwell Institute 4 – 6 30.73 35.95
62 281 Jenkins 3 – 6 – 1 27.44 32.48
71 366 Groves 1 – 9 9.24 39.96
75 395 Windsor Forest 0 – 10 -5.60 41.26
CL OV 4-Div A – AAAAA WL RAT SOS
21 101 Mundy’s Mill 6 – 4 56.90 48.82
41 165 Union Grove 6 – 4 46.64 42.09
44 188 Drew 4 – 6 42.47 45.06
56 259 Ola 2 – 7 31.98 45.19
66 297 Forest Park 2 – 8 25.05 46.58
73 378 Mount Zion (Jonesboro) 0 – 10 4.49 42.10
CL OV 4-Div B – AAAAA WL RAT SOS
2 14 Creekside 9 – 0 83.13 53.73
15 71 McIntosh 8 – 2 63.56 50.17
16 80 Whitewater 7 – 3 61.99 51.95
34 144 Starr’s Mill 4 – 6 49.63 53.52
36 149 Northgate 4 – 6 48.99 54.51
CL OV 5 – AAAAA WL RAT SOS
12 56 North Paulding 9 – 1 67.05 44.31
14 64 South Paulding 9 – 1 64.81 44.86
20 100 Allatoona 7 – 3 57.02 45.16
22 106 East Paulding 7 – 3 55.87 45.56
33 142 New Manchester 6 – 4 50.02 45.97
38 158 Rome 5 – 5 47.46 46.39
43 179 Villa Rica 5 – 5 43.79 46.79
47 207 Woodland (Cartersville) 4 – 6 38.79 47.60
51 237 Paulding County 2 – 8 34.99 47.95
61 276 Hiram 1 – 9 28.76 48.55
69 326 Lithia Springs 0 – 10 18.20 48.74
CL OV 6 – AAAAA WL RAT SOS
7 37 Tucker 10 – 0 73.86 41.13
19 96 Stephenson 8 – 2 59.03 38.71
25 116 Mays 7 – 3 54.69 41.39
30 134 M.L. King 7 – 3 52.20 39.68
35 147 Arabia Mountain 7 – 3 49.33 37.10
50 235 Miller Grove 4 – 6 35.17 41.95
59 265 Dunwoody 3 – 7 31.19 42.68
63 285 Lakeside (Atlanta) 2 – 8 26.81 43.16
64 294 Southwest Dekalb 2 – 8 25.78 43.75
72 376 Clarkston 4 – 5 – 1 6.70 10.71
74 383 North Atlanta 0 – 10 3.12 41.82
CL OV 7-Div A – AAAAA WL RAT SOS
24 114 Sequoyah 8 – 2 55.01 39.98
28 123 Creekview 8 – 2 53.57 39.69
37 153 Northview 5 – 5 48.78 47.37
49 215 Cambridge 3 – 7 37.90 46.62
58 262 Forsyth Central 2 – 8 31.68 44.91
68 320 North Springs 1 – 9 19.43 47.77
CL OV 7-Div B – AAAAA WL RAT SOS
3 22 Kell 10 – 0 79.38 44.90
31 135 Riverwood 7 – 3 52.04 41.23
45 199 Pope 3 – 7 39.97 53.71
54 253 Sprayberry 2 – 8 33.07 50.58
65 295 Osborne 2 – 8 25.61 42.48
CL OV 8 – AAAAA WL RAT SOS
1 11 Gainesville 9 – 1 85.12 51.91
11 54 Flowery Branch 7 – 3 67.76 52.22
18 87 Clarke Central 7 – 3 60.37 50.58
32 138 Heritage (Conyers) 6 – 4 51.42 49.60
40 161 Loganville 5 – 5 47.23 49.35
42 178 Cedar Shoals 3 – 7 44.25 53.47
46 202 Salem 3 – 7 39.52 48.30
52 251 Winder-Barrow 2 – 8 33.38 51.77
67 301 Apalachee 0 – 10 24.09 55.32
CL OV 1 – AAAA WL RAT SOS
16 103 Westover 8 – 2 56.55 42.56
20 117 Cairo 7 – 3 54.35 43.33
31 181 Monroe 6 – 4 43.49 39.43
32 182 Crisp County 6 – 4 43.36 40.45
51 231 Americus-Sumter 4 – 6 35.47 38.60
53 239 Worth County 3 – 7 34.75 41.72
62 307 Albany 2 – 8 22.59 39.15
71 352 Dougherty 0 – 10 13.03 46.46
CL OV 2 – AAAA WL RAT SOS
11 79 Veterans 7 – 3 62.09 50.13
15 98 Mary Persons 9 – 1 57.78 38.04
21 122 Westside (Macon) 7 – 3 53.57 38.20
39 198 Rutland 6 – 4 40.09 33.58
40 204 West Laurens 3 – 7 39.14 46.07
41 205 Baldwin 3 – 7 38.97 47.14
52 236 Perry 3 – 7 35.17 44.82
70 351 Howard 2 – 8 13.36 36.68
CL OV 3-Div A – AAAA WL RAT SOS
6 46 Statesboro 9 – 1 68.98 43.01
8 57 Wayne County 8 – 2 66.82 52.78
19 115 Liberty County 8 – 2 54.85 37.93
48 228 South Effingham 5 – 5 35.67 35.53
CL OV 3-Div B – AAAA WL RAT SOS
7 55 Burke County 7 – 2 67.12 45.17
35 192 Richmond Academy 5 – 5 41.52 41.61
42 213 Glenn Hills 5 – 5 37.98 36.78
67 337 Hephzibah 3 – 7 15.71 30.31
69 345 Butler 1 – 9 13.97 42.89
74 361 Cross Creek 2 – 8 9.79 31.78
CL OV 4-Div A – AAAA WL RAT SOS
9 61 Stockbridge 9 – 1 65.76 43.41
13 92 Eagle’s Landing 8 – 2 59.44 41.97
36 193 Locust Grove 6 – 4 41.45 38.38
44 217 Dutchtown 4 – 6 37.76 42.20
49 229 Woodland (Stockbridge) 2 – 8 35.67 55.12
CL OV 4-Div B – AAAA WL RAT SOS
3 18 Griffin 10 – 0 79.92 37.19
30 175 Jonesboro 6 – 4 44.54 38.19
46 219 Riverdale 4 – 6 37.42 44.15
54 240 Upson-Lee 4 – 6 34.57 42.79
64 315 North Clayton 1 – 9 21.15 44.70
65 318 Spalding 1 – 9 19.57 39.86
CL OV 5 – AAAA WL RAT SOS
1 4 Sandy Creek 9 – 0 – 1 93.27 54.25
2 10 Carrollton 9 – 1 85.58 50.18
10 77 Troup 6 – 4 62.68 53.16
14 95 Alexander 6 – 4 59.19 52.18
26 157 LaGrange 3 – 7 48.17 58.22
37 194 Shaw 4 – 6 41.44 51.78
47 227 Fayette County 1 – 9 36.15 60.02
60 296 Columbus 1 – 9 25.34 48.56
CL OV 6-Div A – AAAA WL RAT SOS
5 42 Marist 8 – 2 71.77 44.15
24 150 Columbia 6 – 4 48.96 43.50
38 197 Chamblee 6 – 4 40.18 35.94
50 230 Lithonia 4 – 6 35.59 38.48
58 277 Stone Mountain 4 – 6 28.28 34.13
68 343 Redan 1 – 9 14.17 46.48
CL OV 6-Div B – AAAA WL RAT SOS
22 127 Carver (Atlanta) 6 – 3 52.72 46.23
25 151 Grady 8 – 2 48.95 34.79
28 164 Banneker 5 – 3 – 1 46.75 39.75
45 218 Washington 6 – 3 37.45 23.15
75 371 Therrell 1 – 9 7.92 40.72
77 394 South Atlanta 0 – 10 -4.41 35.42
CL OV 7-Div A – AAAA WL RAT SOS
27 159 Gilmer 8 – 2 47.42 31.92
29 166 Cedartown 9 – 1 46.61 28.05
43 214 Pickens 6 – 4 37.95 32.13
55 246 Cass 4 – 6 34.12 39.50
63 309 River Ridge 2 – 8 22.06 38.77
CL OV 7-Div B – AAAA WL RAT SOS
17 104 Dalton 8 – 2 56.36 37.19
23 141 Northwest Whitfield 9 – 1 50.27 28.01
59 282 Ridgeland 3 – 7 27.32 37.72
61 306 Southeast Whitfield 4 – 6 23.04 30.46
73 357 LaFayette 2 – 8 11.46 31.65
76 375 Heritage (Ringgold) 1 – 9 6.91 31.00
CL OV 8 – AAAA WL RAT SOS
4 36 Monroe Area 10 – 0 74.69 38.85
12 91 Chestatee 8 – 2 59.83 42.74
18 110 Lanier 8 – 2 55.34 34.41
33 183 Eastside 5 – 5 43.26 44.57
34 191 Stephens County 5 – 5 41.64 40.68
56 255 Madison County 4 – 6 32.87 41.96
57 257 Lumpkin County 2 – 8 32.56 49.26
66 328 Walnut Grove 1 – 9 17.99 43.09
72 355 Johnson (Gainesville) 2 – 8 12.12 32.30
CL OV 1 – AAA WL RAT SOS
17 132 Appling County 8 – 2 52.27 30.77
21 156 Pierce County 9 – 1 48.33 28.26
33 254 Southeast Bulloch 7 – 2 – 1 32.96 22.39
35 268 Johnson (Savannah) 4 – 6 29.94 39.12
39 303 Tattnall County 4 – 6 23.60 29.62
45 327 Brantley County 3 – 7 17.99 27.96
51 342 Savannah 1 – 9 14.29 36.98
52 344 Beach 1 – 9 14.09 36.29
CL OV 2 – AAA WL RAT SOS
12 85 Carver (Columbus) 9 – 1 60.43 31.93
24 185 Peach County 7 – 3 42.91 31.49
34 256 Pike County 5 – 5 32.66 33.00
40 305 Henry County 3 – 7 23.06 39.86
47 332 Jackson 2 – 8 17.55 35.25
54 356 Central (Macon) 1 – 9 11.77 35.13
57 388 Southwest 0 – 10 0.29 31.00
CL OV 3 – AAA WL RAT SOS
2 3 Washington County 9 – 0 94.22 42.60
20 148 Jefferson County 7 – 3 49.23 40.73
22 163 Thomson 3 – 6 46.96 58.09
29 216 Dodge County 4 – 6 37.86 42.45
37 275 Swainsboro 2 – 8 29.08 44.97
CL OV 4 – AAA WL RAT SOS
7 53 Callaway 9 – 1 67.84 40.46
19 146 Central (Carrollton) 8 – 2 49.61 34.25
26 196 B.E.S.T. Academy 6 – 4 40.25 29.62
27 211 Chapel Hill 5 – 5 38.38 38.62
36 269 Maynard Jackson 5 – 5 29.79 28.44
38 289 Rockmart 3 – 7 26.16 39.42
50 340 Haralson County 2 – 8 14.55 34.87
53 349 Douglass 1 – 9 13.66 36.64
CL OV 5 – AAA WL RAT SOS
6 51 Cartersville 10 – 0 68.37 22.45
18 140 Ringgold 8 – 2 50.66 21.90
32 249 Adairsville 7 – 3 33.87 18.22
43 317 Sonoraville 4 – 6 20.59 29.50
44 325 North Murray 4 – 6 18.42 21.27
56 381 Lakeview-Ft. Oglethorpe 3 – 7 4.03 20.74
58 393 Coahulla Creek 2 – 8 -4.17 20.15
59 404 Gordon Central 0 – 10 -18.53 21.79
CL OV 6 – AAA WL RAT SOS
3 40 Woodward Academy 8 – 2 72.01 53.29
4 43 Blessed Trinity 7 – 3 70.25 60.65
5 48 St. Pius X 7 – 3 68.61 57.30
13 94 Cedar Grove 6 – 4 59.27 49.89
15 118 Decatur 7 – 3 54.32 40.66
28 212 Towers 5 – 5 38.08 40.49
46 329 McNair 1 – 9 17.82 50.37
60 407 Cross Keys 1 – 9 -24.09 5.36
CL OV 7 – AAA WL RAT SOS
1 1 Buford 10 – 0 101.65 47.44
8 62 North Hall 8 – 2 65.64 45.90
10 73 White County 8 – 2 63.26 43.17
16 126 West Hall 6 – 4 52.84 43.76
23 174 Dawson County 5 – 5 44.73 46.81
25 195 East Hall 5 – 5 40.62 35.91
42 313 Fannin County 2 – 8 21.30 48.85
55 359 Banks County 0 – 10 10.15 44.43
CL OV 8 – AAA WL RAT SOS
9 67 Hart County 9 – 1 63.96 39.32
11 81 Elbert County 8 – 2 61.53 41.34
14 111 North Oconee 7 – 3 55.31 36.21
30 238 Morgan County 6 – 4 34.93 29.46
31 244 Jackson County 4 – 6 34.17 38.40
41 312 Oconee County 2 – 9 21.48 41.60
48 333 Franklin County 1 – 9 17.07 41.83
49 335 East Jackson 2 – 9 16.01 35.26
CL OV 1 – AA WL RAT SOS
7 74 Brooks County 9 – 1 62.84 44.07
13 109 Thomasville 7 – 3 55.44 46.21
18 167 Cook 6 – 4 46.23 41.69
19 169 Fitzgerald 5 – 4 – 1 45.98 44.31
20 180 Early County 5 – 5 43.50 41.80
30 245 Pelham 4 – 6 34.13 39.37
38 290 Berrien 3 – 7 26.03 35.79
CL OV 2 – AA WL RAT SOS
3 26 Benedictine 10 – 0 77.02 30.49
10 97 Vidalia 9 – 1 57.83 31.07
15 143 Bryan County 8 – 2 49.69 31.98
23 203 Metter 7 – 3 39.34 31.28
29 242 McIntosh County Academy 5 – 5 34.55 34.48
35 272 Bacon County 4 – 6 29.49 35.68
40 298 Toombs County 4 – 6 24.85 30.54
47 347 Long County 2 – 8 13.81 32.41
51 368 Jeff Davis 1 – 9 8.99 33.42
52 370 Atkinson County 1 – 9 7.93 31.89
CL OV 3 – AA WL RAT SOS
12 105 Laney 8 – 2 56.09 28.83
21 190 Dublin 7 – 3 41.86 29.65
31 247 Westside (Augusta) 7 – 3 34.09 20.41
37 288 Screven County 5 – 5 26.37 23.88
49 350 Harlem 6 – 4 13.63 9.82
55 382 East Laurens 1 – 9 3.70 28.29
59 396 Josey 0 – 10 -5.69 32.03
CL OV 4 – AA WL RAT SOS
4 35 Lamar County 10 – 0 74.83 31.00
16 152 Bleckley County 8 – 2 48.93 33.46
24 210 Macon County 6 – 4 38.43 35.09
27 232 Taylor County 6 – 4 35.36 27.79
28 241 Northeast 7 – 3 34.56 21.22
41 300 Putnam County 3 – 7 24.41 35.05
44 321 Monticello 3 – 7 19.17 30.29
62 409 Crawford County 0 – 10 -24.75 29.77
CL OV 5 – AA WL RAT SOS
8 83 Heard County 9 – 1 60.97 29.61
14 131 Bowdon 9 – 1 52.32 26.42
22 201 Kendrick 6 – 4 39.61 33.22
25 225 Manchester 7 – 3 36.86 22.81
26 226 Bremen 5 – 5 36.86 36.92
34 267 Spencer 5 – 5 30.60 32.65
46 339 Jordan 2 – 8 14.67 34.71
53 374 Temple 1 – 9 7.00 30.13
56 384 Chattahoochee County 2 – 8 2.74 27.65
CL OV 6 – AA WL RAT SOS
1 9 Lovett 9 – 1 86.34 66.21
2 24 Greater Atlanta Christian 9 – 1 78.04 33.36
9 88 Westminster 5 – 5 60.26 60.16
43 310 Wesleyan 2 – 8 21.89 39.70
54 379 Hapeville Charter 1 – 8 – 1 4.17 37.34
CL OV 7 – AA WL RAT SOS
5 58 Calhoun 9 – 1 66.41 27.16
32 248 Model 7 – 3 33.96 25.94
33 260 Dade County 7 – 3 31.86 18.27
36 287 Coosa 6 – 4 26.37 18.08
39 292 Pepperell 4 – 6 25.92 30.91
48 348 Chattooga 2 – 8 13.66 32.20
57 385 Armuchee 1 – 9 1.92 25.53
60 403 Murray County 1 – 9 -17.26 21.64
CL OV 8 – AA WL RAT SOS
6 59 Washington-Wilkes 10 – 0 66.26 15.75
11 102 Jefferson 8 – 2 56.80 27.68
17 162 Rabun County 8 – 2 47.11 17.62
42 304 Union County 6 – 4 23.51 18.13
45 338 Riverside Military Academy 5 – 5 14.92 16.69
50 367 Greene County 3 – 7 9.10 25.33
58 389 Oglethorpe County 1 – 9 -1.72 22.44
61 408 Social Circle 0 – 10 -24.61 20.99
CL OV 1 – A WL RAT SOS
2 136 Seminole County 10 – 0 51.91 20.88
25 273 Mitchell County 6 – 4 29.46 22.44
47 346 Miller County 4 – 6 13.92 21.57
51 360 Terrell County 5 – 5 10.03 10.77
53 363 Randolph-Clay 5 – 5 9.66 12.24
59 380 Calhoun County 2 – 8 4.11 20.41
69 401 Baconton Charter 2 – 8 -15.99 9.71
73 410 Stewart County 0 – 10 -32.56 16.73
CL OV 2 – A WL RAT SOS
4 168 Clinch County 7 – 3 46.02 36.43
8 177 Irwin County 8 – 1 – 1 44.36 26.74
16 223 Charlton County 6 – 4 37.08 32.16
27 278 Turner County 4 – 6 28.03 30.88
35 302 Wilcox County 3 – 7 23.89 31.47
41 323 Telfair County 4 – 6 18.65 23.45
54 364 Lanier County 2 – 8 9.30 24.79
CL OV 3-Div A – A WL RAT SOS
5 170 Calvary Day 8 – 2 45.80 23.18
32 291 Savannah Christian 5 – 4 – 1 25.98 23.26
34 299 Claxton 6 – 4 24.78 20.59
39 319 Savannah Country Day 5 – 5 19.53 19.05
44 331 Portal 3 – 6 – 1 17.66 24.63
64 392 Jenkins County 1 – 9 -3.92 20.71
CL OV 3-Div B – A WL RAT SOS
15 222 Johnson County 9 – 1 37.28 14.45
28 279 Emanuel County Institute 6 – 4 27.84 21.30
49 354 Treutlen 4 – 6 12.68 18.25
55 369 Wheeler County 3 – 7 8.25 20.26
58 377 Montgomery County 1 – 9 5.03 23.49
CL OV 4 – A WL RAT SOS
12 189 Marion County 9 – 1 42.00 19.77
13 220 Hawkinsville 7 – 3 37.37 28.14
17 224 Dooly County 7 – 3 36.95 25.36
24 271 Brookstone 6 – 4 29.65 24.83
31 284 Pacelli 7 – 3 26.97 4.66
56 372 Schley County 2 – 8 7.42 23.99
61 387 Greenville 1 – 9 1.29 30.03
71 405 Central (Talbotton) 0 – 10 -22.44 27.84
CL OV 5 – A WL RAT SOS
1 52 Eagle’s Landing Christian 9 – 0 68.37 25.93
7 176 Landmark Christian 8 – 2 44.36 31.38
14 221 Our Lady of Mercy 5 – 5 37.31 35.47
19 234 Holy Innocents 6 – 4 35.30 31.36
22 263 Pace Academy 5 – 5 31.61 27.88
62 390 Strong Rock Christian 3 – 7 -1.85 19.14
67 399 Mount Vernon Presbyterian 1 – 9 -11.49 30.70
CL OV 6-Div A – A WL RAT SOS
11 187 Darlington 8 – 2 42.71 25.38
21 250 Christian Heritage 8 – 2 33.53 14.56
29 280 Trion 5 – 4 27.66 23.11
48 353 Mount Zion (Carroll) 4 – 6 12.70 14.68
52 362 Gordon Lee 3 – 6 9.70 18.08
CL OV 6-Div B – A WL RAT SOS
3 154 Mount Pisgah Christian 10 – 0 48.76 22.53
9 184 Mount Paran Christian 8 – 2 43.08 29.20
36 308 King’s Ridge Christian 4 – 6 22.57 26.47
37 311 Whitefield Academy 4 – 6 21.70 26.27
38 314 Fellowship Christian 5 – 5 21.26 21.59
40 322 St. Francis 8 – 2 19.16 -3.20
46 336 Walker 4 – 5 15.89 21.37
74 411 North Cobb Christian 1 – 9 -37.02 -2.52
CL OV 7 – A WL RAT SOS
10 186 Aquinas 10 – 0 42.74 9.46
20 243 Lincoln County 7 – 3 34.54 16.33
30 283 First Presbyterian 6 – 4 27.28 20.09
42 324 Wilkinson County 4 – 6 18.46 22.47
50 358 Hancock Central 4 – 6 11.17 17.06
57 373 Georgia Military College 4 – 6 7.33 12.53
68 400 Twiggs County 2 – 8 -12.95 20.41
70 402 Glascock County 1 – 9 -16.33 11.80
72 406 Warren County 0 – 10 -22.77 21.17
CL OV 8 – A WL RAT SOS
6 171 Prince Avenue Christian 9 – 1 45.48 18.91
18 233 Commerce 7 – 3 35.32 27.47
23 270 Athens Christian 5 – 5 29.73 29.76
26 274 George Walton Academy 5 – 5 29.09 26.96
33 293 Athens Academy 6 – 4 25.91 22.54
43 330 Pinecrest Academy 5 – 5 17.77 17.32
45 334 Rabun Gap 6 – 4 16.88 -5.80
60 386 Towns County 4 – 6 1.40 12.85
63 391 Hebron Christian Academy 2 – 8 -3.47 20.06
65 397 Lakeview Academy 2 – 7 -6.98 8.51
CL OV 1 – AAAAAA WL RAT SOS
4 7 Camden County 9 – 1 88.47 70.22
5 8 Colquitt County 8 – 2 86.70 72.12
14 25 Lowndes 7 – 3 77.17 63.62
17 31 Valdosta 6 – 4 75.64 69.78
21 38 Tift County 5 – 5 72.41 71.28
22 41 Coffee 5 – 5 71.93 71.27
35 78 Brunswick 3 – 7 62.41 70.39
CL OV 2 – AAAAAA WL RAT SOS
10 17 Lovejoy 9 – 1 80.30 55.92
43 108 Alcovy 7 – 3 55.65 44.38
44 112 Newton 5 – 5 55.27 58.37
48 129 Rockdale County 6 – 4 52.42 50.14
52 139 Luella 5 – 5 50.81 51.31
60 264 Druid Hills 4 – 6 31.29 39.99
61 286 Morrow 1 – 9 26.45 51.47
CL OV 3 – AAAAAA WL RAT SOS
6 12 Langston Hughes 9 – 1 83.60 59.39
26 60 East Coweta 5 – 5 66.20 67.71
31 70 Newnan 6 – 4 63.81 62.51
33 75 Westlake 5 – 5 62.79 62.99
45 120 Douglas County 3 – 7 53.77 62.01
46 124 Tri-Cities 1 – 8 – 1 53.08 64.67
CL OV 4 – AAAAAA WL RAT SOS
7 13 Hillgrove 9 – 1 83.25 62.69
9 16 McEachern 8 – 2 80.52 63.54
12 20 North Cobb 8 – 2 79.91 61.35
15 29 Marietta 8 – 2 76.50 59.43
28 66 Harrison 5 – 5 64.01 65.23
49 130 Campbell 4 – 6 52.33 58.00
51 137 South Cobb 3 – 7 51.56 64.04
56 173 Pebblebrook 3 – 7 44.83 59.69
59 209 Kennesaw Mountain 2 – 8 38.46 60.07
CL OV 5 – AAAAAA WL RAT SOS
18 32 Walton 6 – 4 75.52 70.87
19 33 Milton 8 – 2 75.49 63.23
20 34 Lassiter 7 – 3 75.19 67.14
23 45 Etowah 7 – 3 69.27 62.82
30 69 Cherokee 5 – 5 63.90 64.51
37 84 Woodstock 3 – 7 60.66 66.68
39 90 Roswell 4 – 6 59.90 63.92
40 93 Wheeler 3 – 7 59.43 65.06
CL OV 6 – AAAAAA WL RAT SOS
16 30 Alpharetta 8 – 2 75.87 61.99
24 47 West Forsyth 7 – 3 68.89 60.60
27 65 Lambert 6 – 4 64.56 59.06
34 76 North Forsyth 7 – 3 62.77 55.18
38 89 South Forsyth 7 – 3 60.18 52.98
47 128 Chattahoochee 4 – 6 52.64 58.22
54 155 Centennial 1 – 9 48.49 63.57
57 200 Johns Creek 0 – 10 39.61 62.91
CL OV 7 – AAAAAA WL RAT SOS
1 2 North Gwinnett 9 – 1 94.72 66.44
2 5 Collins Hill 9 – 1 93.10 60.57
3 6 Norcross 8 – 2 92.92 59.97
8 15 Peachtree Ridge 7 – 3 81.99 63.60
11 19 Mill Creek 6 – 4 79.91 64.61
55 172 Duluth 3 – 7 45.17 67.12
58 206 Mountain View 2 – 8 38.97 70.92
62 316 Meadowcreek 1 – 9 20.72 66.89
63 341 Habersham Central 0 – 10 14.37 67.69
CL OV 8 – AAAAAA WL RAT SOS
13 21 Archer 10 – 0 79.74 57.97
25 49 Dacula 6 – 4 68.58 64.36
29 68 Brookwood 5 – 5 63.90 64.53
32 72 Grayson 4 – 6 63.27 65.77
36 82 South Gwinnett 5 – 4 60.99 59.03
41 99 Parkview 3 – 7 57.30 66.14
42 107 Shiloh 5 – 5 55.70 56.04
50 133 Central Gwinnett 2 – 8 52.23 64.26
53 145 Berkmar 3 – 7 49.62 58.19
CL OV 1 – AAAAA WL RAT SOS
5 27 Thomas County Central 8 – 2 76.98 60.37
8 39 Lee County 9 – 1 72.16 39.79
9 44 Harris County 8 – 2 69.67 57.86
29 125 Bainbridge 5 – 5 53.08 56.43
55 258 Hardaway 2 – 8 32.12 55.08
57 261 Northside (Columbus) 2 – 8 31.86 52.27
CL OV 2 – AAAAA WL RAT SOS
4 23 Northside (Warner Robins) 9 – 1 78.75 56.06
6 28 Warner Robins 9 – 1 76.89 54.07
10 50 Houston County 7 – 3 68.51 57.17
17 86 Lakeside (Evans) 6 – 4 60.40 54.55
23 113 Evans 5 – 5 55.05 55.32
27 121 Jones County 5 – 5 53.76 54.26
53 252 Grovetown 2 – 8 33.27 61.67
70 365 Greenbrier 0 – 10 9.25 56.83
CL OV 3 – AAAAA WL RAT SOS
13 63 Ware County 9 – 1 65.40 38.55
26 119 Richmond Hill 9 – 1 53.97 25.92
39 160 Glynn Academy 6 – 4 47.42 39.77
48 208 Effingham County 6 – 4 38.65 36.17
60 266 Bradwell Institute 4 – 6 30.73 35.95
62 281 Jenkins 3 – 6 – 1 27.44 32.48
71 366 Groves 1 – 9 9.24 39.96
75 395 Windsor Forest 0 – 10 -5.60 41.26
CL OV 4-Div A – AAAAA WL RAT SOS
21 101 Mundy’s Mill 6 – 4 56.90 48.82
41 165 Union Grove 6 – 4 46.64 42.09
44 188 Drew 4 – 6 42.47 45.06
56 259 Ola 2 – 7 31.98 45.19
66 297 Forest Park 2 – 8 25.05 46.58
73 378 Mount Zion (Jonesboro) 0 – 10 4.49 42.10
CL OV 4-Div B – AAAAA WL RAT SOS
2 14 Creekside 9 – 0 83.13 53.73
15 71 McIntosh 8 – 2 63.56 50.17
16 80 Whitewater 7 – 3 61.99 51.95
34 144 Starr’s Mill 4 – 6 49.63 53.52
36 149 Northgate 4 – 6 48.99 54.51
CL OV 5 – AAAAA WL RAT SOS
12 56 North Paulding 9 – 1 67.05 44.31
14 64 South Paulding 9 – 1 64.81 44.86
20 100 Allatoona 7 – 3 57.02 45.16
22 106 East Paulding 7 – 3 55.87 45.56
33 142 New Manchester 6 – 4 50.02 45.97
38 158 Rome 5 – 5 47.46 46.39
43 179 Villa Rica 5 – 5 43.79 46.79
47 207 Woodland (Cartersville) 4 – 6 38.79 47.60
51 237 Paulding County 2 – 8 34.99 47.95
61 276 Hiram 1 – 9 28.76 48.55
69 326 Lithia Springs 0 – 10 18.20 48.74
CL OV 6 – AAAAA WL RAT SOS
7 37 Tucker 10 – 0 73.86 41.13
19 96 Stephenson 8 – 2 59.03 38.71
25 116 Mays 7 – 3 54.69 41.39
30 134 M.L. King 7 – 3 52.20 39.68
35 147 Arabia Mountain 7 – 3 49.33 37.10
50 235 Miller Grove 4 – 6 35.17 41.95
59 265 Dunwoody 3 – 7 31.19 42.68
63 285 Lakeside (Atlanta) 2 – 8 26.81 43.16
64 294 Southwest Dekalb 2 – 8 25.78 43.75
72 376 Clarkston 4 – 5 – 1 6.70 10.71
74 383 North Atlanta 0 – 10 3.12 41.82
CL OV 7-Div A – AAAAA WL RAT SOS
24 114 Sequoyah 8 – 2 55.01 39.98
28 123 Creekview 8 – 2 53.57 39.69
37 153 Northview 5 – 5 48.78 47.37
49 215 Cambridge 3 – 7 37.90 46.62
58 262 Forsyth Central 2 – 8 31.68 44.91
68 320 North Springs 1 – 9 19.43 47.77
CL OV 7-Div B – AAAAA WL RAT SOS
3 22 Kell 10 – 0 79.38 44.90
31 135 Riverwood 7 – 3 52.04 41.23
45 199 Pope 3 – 7 39.97 53.71
54 253 Sprayberry 2 – 8 33.07 50.58
65 295 Osborne 2 – 8 25.61 42.48
CL OV 8 – AAAAA WL RAT SOS
1 11 Gainesville 9 – 1 85.12 51.91
11 54 Flowery Branch 7 – 3 67.76 52.22
18 87 Clarke Central 7 – 3 60.37 50.58
32 138 Heritage (Conyers) 6 – 4 51.42 49.60
40 161 Loganville 5 – 5 47.23 49.35
42 178 Cedar Shoals 3 – 7 44.25 53.47
46 202 Salem 3 – 7 39.52 48.30
52 251 Winder-Barrow 2 – 8 33.38 51.77
67 301 Apalachee 0 – 10 24.09 55.32
CL OV 1 – AAAA WL RAT SOS
16 103 Westover 8 – 2 56.55 42.56
20 117 Cairo 7 – 3 54.35 43.33
31 181 Monroe 6 – 4 43.49 39.43
32 182 Crisp County 6 – 4 43.36 40.45
51 231 Americus-Sumter 4 – 6 35.47 38.60
53 239 Worth County 3 – 7 34.75 41.72
62 307 Albany 2 – 8 22.59 39.15
71 352 Dougherty 0 – 10 13.03 46.46
CL OV 2 – AAAA WL RAT SOS
11 79 Veterans 7 – 3 62.09 50.13
15 98 Mary Persons 9 – 1 57.78 38.04
21 122 Westside (Macon) 7 – 3 53.57 38.20
39 198 Rutland 6 – 4 40.09 33.58
40 204 West Laurens 3 – 7 39.14 46.07
41 205 Baldwin 3 – 7 38.97 47.14
52 236 Perry 3 – 7 35.17 44.82
70 351 Howard 2 – 8 13.36 36.68
CL OV 3-Div A – AAAA WL RAT SOS
6 46 Statesboro 9 – 1 68.98 43.01
8 57 Wayne County 8 – 2 66.82 52.78
19 115 Liberty County 8 – 2 54.85 37.93
48 228 South Effingham 5 – 5 35.67 35.53
CL OV 3-Div B – AAAA WL RAT SOS
7 55 Burke County 7 – 2 67.12 45.17
35 192 Richmond Academy 5 – 5 41.52 41.61
42 213 Glenn Hills 5 – 5 37.98 36.78
67 337 Hephzibah 3 – 7 15.71 30.31
69 345 Butler 1 – 9 13.97 42.89
74 361 Cross Creek 2 – 8 9.79 31.78
CL OV 4-Div A – AAAA WL RAT SOS
9 61 Stockbridge 9 – 1 65.76 43.41
13 92 Eagle’s Landing 8 – 2 59.44 41.97
36 193 Locust Grove 6 – 4 41.45 38.38
44 217 Dutchtown 4 – 6 37.76 42.20
49 229 Woodland (Stockbridge) 2 – 8 35.67 55.12
CL OV 4-Div B – AAAA WL RAT SOS
3 18 Griffin 10 – 0 79.92 37.19
30 175 Jonesboro 6 – 4 44.54 38.19
46 219 Riverdale 4 – 6 37.42 44.15
54 240 Upson-Lee 4 – 6 34.57 42.79
64 315 North Clayton 1 – 9 21.15 44.70
65 318 Spalding 1 – 9 19.57 39.86
CL OV 5 – AAAA WL RAT SOS
1 4 Sandy Creek 9 – 0 – 1 93.27 54.25
2 10 Carrollton 9 – 1 85.58 50.18
10 77 Troup 6 – 4 62.68 53.16
14 95 Alexander 6 – 4 59.19 52.18
26 157 LaGrange 3 – 7 48.17 58.22
37 194 Shaw 4 – 6 41.44 51.78
47 227 Fayette County 1 – 9 36.15 60.02
60 296 Columbus 1 – 9 25.34 48.56
CL OV 6-Div A – AAAA WL RAT SOS
5 42 Marist 8 – 2 71.77 44.15
24 150 Columbia 6 – 4 48.96 43.50
38 197 Chamblee 6 – 4 40.18 35.94
50 230 Lithonia 4 – 6 35.59 38.48
58 277 Stone Mountain 4 – 6 28.28 34.13
68 343 Redan 1 – 9 14.17 46.48
CL OV 6-Div B – AAAA WL RAT SOS
22 127 Carver (Atlanta) 6 – 3 52.72 46.23
25 151 Grady 8 – 2 48.95 34.79
28 164 Banneker 5 – 3 – 1 46.75 39.75
45 218 Washington 6 – 3 37.45 23.15
75 371 Therrell 1 – 9 7.92 40.72
77 394 South Atlanta 0 – 10 -4.41 35.42
CL OV 7-Div A – AAAA WL RAT SOS
27 159 Gilmer 8 – 2 47.42 31.92
29 166 Cedartown 9 – 1 46.61 28.05
43 214 Pickens 6 – 4 37.95 32.13
55 246 Cass 4 – 6 34.12 39.50
63 309 River Ridge 2 – 8 22.06 38.77
CL OV 7-Div B – AAAA WL RAT SOS
17 104 Dalton 8 – 2 56.36 37.19
23 141 Northwest Whitfield 9 – 1 50.27 28.01
59 282 Ridgeland 3 – 7 27.32 37.72
61 306 Southeast Whitfield 4 – 6 23.04 30.46
73 357 LaFayette 2 – 8 11.46 31.65
76 375 Heritage (Ringgold) 1 – 9 6.91 31.00
CL OV 8 – AAAA WL RAT SOS
4 36 Monroe Area 10 – 0 74.69 38.85
12 91 Chestatee 8 – 2 59.83 42.74
18 110 Lanier 8 – 2 55.34 34.41
33 183 Eastside 5 – 5 43.26 44.57
34 191 Stephens County 5 – 5 41.64 40.68
56 255 Madison County 4 – 6 32.87 41.96
57 257 Lumpkin County 2 – 8 32.56 49.26
66 328 Walnut Grove 1 – 9 17.99 43.09
72 355 Johnson (Gainesville) 2 – 8 12.12 32.30
CL OV 1 – AAA WL RAT SOS
17 132 Appling County 8 – 2 52.27 30.77
21 156 Pierce County 9 – 1 48.33 28.26
33 254 Southeast Bulloch 7 – 2 – 1 32.96 22.39
35 268 Johnson (Savannah) 4 – 6 29.94 39.12
39 303 Tattnall County 4 – 6 23.60 29.62
45 327 Brantley County 3 – 7 17.99 27.96
51 342 Savannah 1 – 9 14.29 36.98
52 344 Beach 1 – 9 14.09 36.29
CL OV 2 – AAA WL RAT SOS
12 85 Carver (Columbus) 9 – 1 60.43 31.93
24 185 Peach County 7 – 3 42.91 31.49
34 256 Pike County 5 – 5 32.66 33.00
40 305 Henry County 3 – 7 23.06 39.86
47 332 Jackson 2 – 8 17.55 35.25
54 356 Central (Macon) 1 – 9 11.77 35.13
57 388 Southwest 0 – 10 0.29 31.00
CL OV 3 – AAA WL RAT SOS
2 3 Washington County 9 – 0 94.22 42.60
20 148 Jefferson County 7 – 3 49.23 40.73
22 163 Thomson 3 – 6 46.96 58.09
29 216 Dodge County 4 – 6 37.86 42.45
37 275 Swainsboro 2 – 8 29.08 44.97
CL OV 4 – AAA WL RAT SOS
7 53 Callaway 9 – 1 67.84 40.46
19 146 Central (Carrollton) 8 – 2 49.61 34.25
26 196 B.E.S.T. Academy 6 – 4 40.25 29.62
27 211 Chapel Hill 5 – 5 38.38 38.62
36 269 Maynard Jackson 5 – 5 29.79 28.44
38 289 Rockmart 3 – 7 26.16 39.42
50 340 Haralson County 2 – 8 14.55 34.87
53 349 Douglass 1 – 9 13.66 36.64
CL OV 5 – AAA WL RAT SOS
6 51 Cartersville 10 – 0 68.37 22.45
18 140 Ringgold 8 – 2 50.66 21.90
32 249 Adairsville 7 – 3 33.87 18.22
43 317 Sonoraville 4 – 6 20.59 29.50
44 325 North Murray 4 – 6 18.42 21.27
56 381 Lakeview-Ft. Oglethorpe 3 – 7 4.03 20.74
58 393 Coahulla Creek 2 – 8 -4.17 20.15
59 404 Gordon Central 0 – 10 -18.53 21.79
CL OV 6 – AAA WL RAT SOS
3 40 Woodward Academy 8 – 2 72.01 53.29
4 43 Blessed Trinity 7 – 3 70.25 60.65
5 48 St. Pius X 7 – 3 68.61 57.30
13 94 Cedar Grove 6 – 4 59.27 49.89
15 118 Decatur 7 – 3 54.32 40.66
28 212 Towers 5 – 5 38.08 40.49
46 329 McNair 1 – 9 17.82 50.37
60 407 Cross Keys 1 – 9 -24.09 5.36
CL OV 7 – AAA WL RAT SOS
1 1 Buford 10 – 0 101.65 47.44
8 62 North Hall 8 – 2 65.64 45.90
10 73 White County 8 – 2 63.26 43.17
16 126 West Hall 6 – 4 52.84 43.76
23 174 Dawson County 5 – 5 44.73 46.81
25 195 East Hall 5 – 5 40.62 35.91
42 313 Fannin County 2 – 8 21.30 48.85
55 359 Banks County 0 – 10 10.15 44.43
CL OV 8 – AAA WL RAT SOS
9 67 Hart County 9 – 1 63.96 39.32
11 81 Elbert County 8 – 2 61.53 41.34
14 111 North Oconee 7 – 3 55.31 36.21
30 238 Morgan County 6 – 4 34.93 29.46
31 244 Jackson County 4 – 6 34.17 38.40
41 312 Oconee County 2 – 9 21.48 41.60
48 333 Franklin County 1 – 9 17.07 41.83
49 335 East Jackson 2 – 9 16.01 35.26
CL OV 1 – AA WL RAT SOS
7 74 Brooks County 9 – 1 62.84 44.07
13 109 Thomasville 7 – 3 55.44 46.21
18 167 Cook 6 – 4 46.23 41.69
19 169 Fitzgerald 5 – 4 – 1 45.98 44.31
20 180 Early County 5 – 5 43.50 41.80
30 245 Pelham 4 – 6 34.13 39.37
38 290 Berrien 3 – 7 26.03 35.79
CL OV 2 – AA WL RAT SOS
3 26 Benedictine 10 – 0 77.02 30.49
10 97 Vidalia 9 – 1 57.83 31.07
15 143 Bryan County 8 – 2 49.69 31.98
23 203 Metter 7 – 3 39.34 31.28
29 242 McIntosh County Academy 5 – 5 34.55 34.48
35 272 Bacon County 4 – 6 29.49 35.68
40 298 Toombs County 4 – 6 24.85 30.54
47 347 Long County 2 – 8 13.81 32.41
51 368 Jeff Davis 1 – 9 8.99 33.42
52 370 Atkinson County 1 – 9 7.93 31.89
CL OV 3 – AA WL RAT SOS
12 105 Laney 8 – 2 56.09 28.83
21 190 Dublin 7 – 3 41.86 29.65
31 247 Westside (Augusta) 7 – 3 34.09 20.41
37 288 Screven County 5 – 5 26.37 23.88
49 350 Harlem 6 – 4 13.63 9.82
55 382 East Laurens 1 – 9 3.70 28.29
59 396 Josey 0 – 10 -5.69 32.03
CL OV 4 – AA WL RAT SOS
4 35 Lamar County 10 – 0 74.83 31.00
16 152 Bleckley County 8 – 2 48.93 33.46
24 210 Macon County 6 – 4 38.43 35.09
27 232 Taylor County 6 – 4 35.36 27.79
28 241 Northeast 7 – 3 34.56 21.22
41 300 Putnam County 3 – 7 24.41 35.05
44 321 Monticello 3 – 7 19.17 30.29
62 409 Crawford County 0 – 10 -24.75 29.77
CL OV 5 – AA WL RAT SOS
8 83 Heard County 9 – 1 60.97 29.61
14 131 Bowdon 9 – 1 52.32 26.42
22 201 Kendrick 6 – 4 39.61 33.22
25 225 Manchester 7 – 3 36.86 22.81
26 226 Bremen 5 – 5 36.86 36.92
34 267 Spencer 5 – 5 30.60 32.65
46 339 Jordan 2 – 8 14.67 34.71
53 374 Temple 1 – 9 7.00 30.13
56 384 Chattahoochee County 2 – 8 2.74 27.65
CL OV 6 – AA WL RAT SOS
1 9 Lovett 9 – 1 86.34 66.21
2 24 Greater Atlanta Christian 9 – 1 78.04 33.36
9 88 Westminster 5 – 5 60.26 60.16
43 310 Wesleyan 2 – 8 21.89 39.70
54 379 Hapeville Charter 1 – 8 – 1 4.17 37.34
CL OV 7 – AA WL RAT SOS
5 58 Calhoun 9 – 1 66.41 27.16
32 248 Model 7 – 3 33.96 25.94
33 260 Dade County 7 – 3 31.86 18.27
36 287 Coosa 6 – 4 26.37 18.08
39 292 Pepperell 4 – 6 25.92 30.91
48 348 Chattooga 2 – 8 13.66 32.20
57 385 Armuchee 1 – 9 1.92 25.53
60 403 Murray County 1 – 9 -17.26 21.64
CL OV 8 – AA WL RAT SOS
6 59 Washington-Wilkes 10 – 0 66.26 15.75
11 102 Jefferson 8 – 2 56.80 27.68
17 162 Rabun County 8 – 2 47.11 17.62
42 304 Union County 6 – 4 23.51 18.13
45 338 Riverside Military Academy 5 – 5 14.92 16.69
50 367 Greene County 3 – 7 9.10 25.33
58 389 Oglethorpe County 1 – 9 -1.72 22.44
61 408 Social Circle 0 – 10 -24.61 20.99
CL OV 1 – A WL RAT SOS
2 136 Seminole County 10 – 0 51.91 20.88
25 273 Mitchell County 6 – 4 29.46 22.44
47 346 Miller County 4 – 6 13.92 21.57
51 360 Terrell County 5 – 5 10.03 10.77
53 363 Randolph-Clay 5 – 5 9.66 12.24
59 380 Calhoun County 2 – 8 4.11 20.41
69 401 Baconton Charter 2 – 8 -15.99 9.71
73 410 Stewart County 0 – 10 -32.56 16.73
CL OV 2 – A WL RAT SOS
4 168 Clinch County 7 – 3 46.02 36.43
8 177 Irwin County 8 – 1 – 1 44.36 26.74
16 223 Charlton County 6 – 4 37.08 32.16
27 278 Turner County 4 – 6 28.03 30.88
35 302 Wilcox County 3 – 7 23.89 31.47
41 323 Telfair County 4 – 6 18.65 23.45
54 364 Lanier County 2 – 8 9.30 24.79
CL OV 3-Div A – A WL RAT SOS
5 170 Calvary Day 8 – 2 45.80 23.18
32 291 Savannah Christian 5 – 4 – 1 25.98 23.26
34 299 Claxton 6 – 4 24.78 20.59
39 319 Savannah Country Day 5 – 5 19.53 19.05
44 331 Portal 3 – 6 – 1 17.66 24.63
64 392 Jenkins County 1 – 9 -3.92 20.71
CL OV 3-Div B – A WL RAT SOS
15 222 Johnson County 9 – 1 37.28 14.45
28 279 Emanuel County Institute 6 – 4 27.84 21.30
49 354 Treutlen 4 – 6 12.68 18.25
55 369 Wheeler County 3 – 7 8.25 20.26
58 377 Montgomery County 1 – 9 5.03 23.49
CL OV 4 – A WL RAT SOS
12 189 Marion County 9 – 1 42.00 19.77
13 220 Hawkinsville 7 – 3 37.37 28.14
17 224 Dooly County 7 – 3 36.95 25.36
24 271 Brookstone 6 – 4 29.65 24.83
31 284 Pacelli 7 – 3 26.97 4.66
56 372 Schley County 2 – 8 7.42 23.99
61 387 Greenville 1 – 9 1.29 30.03
71 405 Central (Talbotton) 0 – 10 -22.44 27.84
CL OV 5 – A WL RAT SOS
1 52 Eagle’s Landing Christian 9 – 0 68.37 25.93
7 176 Landmark Christian 8 – 2 44.36 31.38
14 221 Our Lady of Mercy 5 – 5 37.31 35.47
19 234 Holy Innocents 6 – 4 35.30 31.36
22 263 Pace Academy 5 – 5 31.61 27.88
62 390 Strong Rock Christian 3 – 7 -1.85 19.14
67 399 Mount Vernon Presbyterian 1 – 9 -11.49 30.70
CL OV 6-Div A – A WL RAT SOS
11 187 Darlington 8 – 2 42.71 25.38
21 250 Christian Heritage 8 – 2 33.53 14.56
29 280 Trion 5 – 4 27.66 23.11
48 353 Mount Zion (Carroll) 4 – 6 12.70 14.68
52 362 Gordon Lee 3 – 6 9.70 18.08
CL OV 6-Div B – A WL RAT SOS
3 154 Mount Pisgah Christian 10 – 0 48.76 22.53
9 184 Mount Paran Christian 8 – 2 43.08 29.20
36 308 King’s Ridge Christian 4 – 6 22.57 26.47
37 311 Whitefield Academy 4 – 6 21.70 26.27
38 314 Fellowship Christian 5 – 5 21.26 21.59
40 322 St. Francis 8 – 2 19.16 -3.20
46 336 Walker 4 – 5 15.89 21.37
74 411 North Cobb Christian 1 – 9 -37.02 -2.52
CL OV 7 – A WL RAT SOS
10 186 Aquinas 10 – 0 42.74 9.46
20 243 Lincoln County 7 – 3 34.54 16.33
30 283 First Presbyterian 6 – 4 27.28 20.09
42 324 Wilkinson County 4 – 6 18.46 22.47
50 358 Hancock Central 4 – 6 11.17 17.06
57 373 Georgia Military College 4 – 6 7.33 12.53
68 400 Twiggs County 2 – 8 -12.95 20.41
70 402 Glascock County 1 – 9 -16.33 11.80
72 406 Warren County 0 – 10 -22.77 21.17
CL OV 8 – A WL RAT SOS
6 171 Prince Avenue Christian 9 – 1 45.48 18.91
18 233 Commerce 7 – 3 35.32 27.47
23 270 Athens Christian 5 – 5 29.73 29.76
26 274 George Walton Academy 5 – 5 29.09 26.96
33 293 Athens Academy 6 – 4 25.91 22.54
43 330 Pinecrest Academy 5 – 5 17.77 17.32
45 334 Rabun Gap 6 – 4 16.88 -5.80
60 386 Towns County 4 – 6 1.40 12.85
63 391 Hebron Christian Academy 2 – 8 -3.47 20.06
65 397 Lakeview Academy 2 – 7 -6.98 8.51

44 comments Add your comment

region4fan

November 13th, 2013
4:31 pm

Can anyone explain the rating of Langston Hughes? They were losers fairly handily at their place last year to North Cobb and I thought there were a lot of senior starters on that team.

Sportsnut

November 13th, 2013
4:41 pm

@region4fan == They don’t know! Understand that the ratings are based on what someone believes from the start. They are a non factor.

RobFromNorcross

November 13th, 2013
4:50 pm

So the three highest-ranked teams that didn’t make the playoffs in AAAAAA

11 Mill Creek (R7)
21 Tift (R1)
22 Coffee (R1)

And three worst-ranked teams to make the playoffs:

52 Luella (R2)
44 Newton (R2)
43 Alcovy (R2)

RobFromNorcross

November 13th, 2013
4:58 pm

@Sprotsknot- You just saw Loren comment on this on the other thread but you obviously have trouble with the English language, These rankings are based only on actual games played this year and are not based on what the preseason “beliefs” were. At this point in the season there are enough games played giving actual results that there is no need to make any estimates about team strength. These are the computer models best estimate to take all the games played THIS YEAR.

Loren Maxwell

November 13th, 2013
6:28 pm

@region4fan: “Can anyone explain the rating of Langston Hughes? They were losers fairly handily at their place last year to North Cobb and I thought there were a lot of senior starters on that team.”

Their rating is based on the scores and schedules of this season. Last year’s game to North Cobb and the amount of seniors they’ve returned are not factored. Past ratings have an impact earlier in the season to help establish the early season ratings, but at this point the impact is negligible.

@Sportsnut: “They don’t know!”

Actually, Sportsnut, I am well familiar with how the ratings are calculated and don’t mind personally fielding those types of questions :-)

Carlos

November 13th, 2013
7:29 pm

Whats up with Langston Hughes? They got their heads handed to them by North Cobb in the playoff last year and graduated a ton of players. How can they be ranked in the top 10?

yo

November 13th, 2013
7:46 pm

These 4 don’t make sense to me. Strength of Schedule highest for #4 but #4 has lowest RAT.

1 2 North Gwinnett 9 – 1 94.72 66.44
2 5 Collins Hill 9 – 1 93.10 60.57
3 6 Norcross 8 – 2 92.92 59.97
4 7 Camden County 9 – 1 88.47 70.22

Records are basically the same and highest strength of schedule has lowest ranking?

RobFromNorcross

November 13th, 2013
7:57 pm

Strange year… it is all about matchups and who comes ready to play

Examples:
#4 lost to #1 by 20
#3 lost to #1 by 19
#1 lost to #2 by 25
#2 lost to #3 by 28

the game still needs to be played on the field…

Todd Holcomb

November 13th, 2013
10:26 pm

Yo – Great question. I’m sure much of the answer lies in the fact that the ratings consider margin of victory. Camden County has beaten Brunswick, Coffee and Valdosta – teams that are pretty good, but nothing special really – by 4, 6 and 7 points.

Here is something else that Loren explained to me that helped me understand the ratings better.

First of all, if one team is rated 90 and another 80, that means one would be expected to beat the other by 10 points if they played. Now, let’s say those two teams do play and the favorite wins by 15. The prediction is off by 5. Call it an error total of 5. Question for the computer: What ratings would each of the 412 teams need to have in order to minimize that error total for a whole season of scores?

That’s the problem that Loren’s model attempts to solve. The ratings are the best possible mathematical ”explanation” for the scores and point spreads that have occurred.

Keep in mind that Camden is rated 10-20 points better than Coffee, Brunswick, Valdosta. But if you raised Camden’s rating to account for that (or raised Coffee’s instead, for example), it would better explain Valdosta-Coffe, but it create a ripple effect that then fails to explain many more scores as a result.

The ratings are the best explanation for all of the scores and all of the contradictions to date, some 2,000+ games.

FridayNightLightsFan

November 13th, 2013
10:47 pm

Buford = 101.65 Whats the highest rating todate?

New Kids On The Block

November 13th, 2013
10:55 pm

@region 4fan

Last year was last year. These rankings are for this year, and this year only. We have only been open for 5 years and look forward to being an annual thorn in everyone’s side. And to answer your question, no Langston Hughes did not graduate a lot of players last year. The majority of the team were sophomores.

Loren Maxwell

November 13th, 2013
11:11 pm

@yo: “Records are basically the same and highest strength of schedule has lowest ranking?”

Todd is correct, margin of victory accounts for the difference.

Loren Maxwell

November 13th, 2013
11:15 pm

@FridayNightLightsFan: “Buford = 101.65 Whats the highest rating todate?”

If you mean all-time, then 114.48 with 1971 Valdosta.

Suwanee 0wns

November 14th, 2013
12:04 am

Wow! So this is actually something like a “least squares” fit to the margin of difference? That just makes too much sense. Thank you Mr. Maxwell. Do you do anything special about the outliers if say Lowndes beats the girls’ school for the blind by 87 to 0? I hate to think any team gets credit for running up the score on teams they never should have played in the first place.

Just thinking … these threads with mathematical predictions should come with some sort of warning like “SPOILER ALERT”. That way some Region 2 fans will know not to open them and view the numbers. Otherwise it is just too difficult to continue to be oblivious to reality once you have seen the objective numbers.

Perhaps someone can confirm how this works for me with a hypothetical example. Let’s say that Lovejoy was going to play Marietta? Then the best prediction of margin of victory would be 80.30 – 76.50 = 3.80; or a 3 – 4 point margin of victory for Lovejoy? Wow! That is way closer than I would expect. That is just: a single dropped punt, one field goal missed or made, a fumble on the 5 yd line, or a completed pass to a TE on a drag pattern where the blitzing linebacker should have been. Who knew that could be such a close game? Good thing for Lovejoy that they play perfect games without any errors.

Mr. Maxwell, do you compute standard deviation by team? Or for the whole model? Any chance you can share the standard deviation of the errors for the whole model? Using that number it seems like you could almost do a Monte Carlo simulation to predict the number of upsets (lower seeded team beating a higher seeded team) to be expected in the first round. That might be a good test of the model? Any idea what percentage of games over the entire season are predicted correctly? Thank you so much for the effort and thought that went into this. And thank you for sharing it with us.

It is REALLY interesting to look at some of the potential matchups between teams. Some of these games on Friday will be more fun to watch than I even imagined. This is as much fun as watching any team beat the Yankees.

Suwanee 0wns

November 14th, 2013
12:13 am

Note: I get a special joy watching the Yankees lose since my wife is from New York, although I can never admit it. You might even say I Love the Joy I get when they lose.

Loren Maxwell

November 14th, 2013
4:08 am

@Suwanee 0wns: “So this is actually something like a ‘least squares’ fit to the margin of difference?”Similar, except that it’s based on a logistic regression rather than a linear regression.”Do you do anything special about the outliers if say Lowndes beats the girls’ school for the blind by 87 to 0?”Using a logistic regression model naturally discounts run away scores.  Logistic regression views a 70 point victory and a tie much differently than two 35 point victories.   A linear model like least squares would treat them as identical situations.  I don’t believe least squares to be well suited for most sports ratings.”these threads with mathematical predictions should come with some sort of warning like ‘SPOILER ALERT’”Thanks!”Perhaps someone can confirm how this works for me with a hypothetical example. Let’s say that Lovejoy was going to play Marietta? Then the best prediction of margin of victory would be 80.30 – 76.50 = 3.80; or a 3 – 4 point margin of victory for Lovejoy?”That’s how it works in general.  However, the system also calculates a home advantage (currently 1.01 points but historically around 1.5 points) and also dampens the projected margins to account for the fact that most teams who are 80 points better will not actually tack on 80 points.  The formula for the projected margins start to taper the numbers off at around 27 points and then tops them out at 42.  This fomula came from an analysis I did on 2010 and 2011 scores and they matched what typically happened on the field with mismatched teams.”do you compute standard deviation by team?”I could compute one so that we’d see something like Buford being rated 101.65 +- 7.46 with a 95% confidence interval, however I have never included that.  Might make for some interesting insights!”for the whole model? Any chance you can share the standard deviation of the errors for the whole model?”Any regression automatically relies on calculating the error of the whole model, but I imagine you’re asking about the standard deviation for single game.  I don’t actively calculate it, but in the past I have measured about a 17 point standard deviation for high school football.  Although I haven’t done a rigorous analysis, my guess is that most variation comes from mismatched teams.  A team that should win by 40 but wins by 70 adds just as much variation as a team that should tie but wins by 30, although intuitively we know the first team is running up the score.”Using that number it seems like you could almost do a Monte Carlo simulation to predict the number of upsets (lower seeded team beating a higher seeded team) to be expected in the first round.”Not exactly in the way you suggest, but I actually run a Monte Carlo simulation for the playoffs.  I think those odds will be posted later today.  It doesn’t show the expected upsets per round like what you might see for the NCAA basketball tournament, but in that case they are grouping teams by seed to get those numbers.  In my simulations, I’m using the actual ratings.”That might be a good test of the model?”Any regression model is measured by minimizing the total error in the model (or actually maximing the likelihood).Essentially, using an initial set of ratings, a likelihood for each game is calculated and then the likelihood for all the games is totaled.  For example, if Team A, Team B, and Team C start with the same initial rating, then a 42-0 victory by Team A would be unlikely but a 21-21 tie between Team B and Team C would be highly likely.  If there can be any change to the ratings that increase the total likelihood of the results seen on the field, then the program makes those changes and repeats the process of totaling the likelihood again and making further changes to increase the likelihood.  This is repeated until there are no more changes that can be made to the ratings that will significantly increase the likelihood.  Using the example again, after a series of changes we would naturally expect Team A to end up 42 points better than Team B, who would naturally have the same rating as Team C.  However, things get more complicated if Team A plays Team C and only wins 28-14, since now it is not possible to come up with a perfect answer.  However, the process generate the ratings that provide the most likely explaination for the on field results.”Any idea what percentage of games over the entire season are predicted correctly?”This is another number I don’t actively calculate, but I’ve sporadically checked in the past and I think I normally get around 80-85% correct for winners with the 17 point error.  Although it tends to have the highest standard deviation, high school football also tends to have the highest prediction percentage since there are many more mismatches than college or the NFL.  I’ve tinkered with the system enough and seen enough of others’ to know those numbers will be hard to beat on a consistent basis.”Thank you so much for the effort and thought that went into this. And thank you for sharing it with us.It is REALLY interesting to look at some of the potential matchups between teams. Some of these games on Friday will be more fun to watch than I even imagined.”Thanks for the kind words!  I’m really grateful to Todd and the AJC for the opportunity to publish something I enjoy and to interact with people who have a sincere interest in what I’m doing.  Admittedly, the ratings are far from the only way to look at GHSA teams, but I think they can provide a valuable layer of information, especially when considering I rate and rank 412 teams and 48 regions and provide projected margins for approximately 220 games on a weekly basis, plus generate playoff odds for 176 teams through five rounds of the playoffs.  It’s simply too much data for anyone to truly do a deep player-by-player and coach-by-coach analysis for.

Loren Maxwell

November 14th, 2013
4:21 am

Not sure why my previous post formatted that way, but I’m reposting in the hopes that it will keep the returns in this time (plus I get to correct three typos I found!):

@Suwanee 0wns: “So this is actually something like a ‘least squares’ fit to the margin of difference?”

Similar, except that it’s based on a logistic regression rather than a linear regression.

“Do you do anything special about the outliers if say Lowndes beats the girls’ school for the blind by 87 to 0?”

Using a logistic regression model naturally discounts run away scores. Logistic regression views a 70 point victory and a tie much differently than two 35 point victories. A linear model like least squares would treat them as identical situations. I don’t believe least squares to be well suited for most sports ratings.

“these threads with mathematical predictions should come with some sort of warning like ‘SPOILER ALERT’”

Thanks!

“Perhaps someone can confirm how this works for me with a hypothetical example. Let’s say that Lovejoy was going to play Marietta? Then the best prediction of margin of victory would be 80.30 – 76.50 = 3.80; or a 3 – 4 point margin of victory for Lovejoy?”

That’s how it works in general. However, the system also calculates a home advantage (currently 1.01 points but historically around 1.5 points) and also dampens the projected margins to account for the fact that most teams who are 80 points better will not actually tack on 80 points. The formula for the projected margins start to taper the numbers off at around 27 points and then tops them out at 42. This formula came from an analysis I did on 2010 and 2011 scores and they matched what typically happened on the field with mismatched teams.

“do you compute standard deviation by team?”

I could compute one so that we’d see something like Buford being rated 101.65 +- 7.46 with a 95% confidence interval, however I have never included that. Might make for some interesting insights!

“for the whole model? Any chance you can share the standard deviation of the errors for the whole model?”

Any regression automatically relies on calculating the error of the whole model, but I imagine you’re asking about the standard deviation for single game. I don’t actively calculate it, but in the past I have measured about a 17 point standard deviation for high school football. Although I haven’t done a rigorous analysis, my guess is that most variation comes from mismatched teams. A team that should win by 40 but wins by 70 adds just as much variation as a team that should tie but wins by 30, although intuitively we know the first team is running up the score.

“Using that number it seems like you could almost do a Monte Carlo simulation to predict the number of upsets (lower seeded team beating a higher seeded team) to be expected in the first round.”

Not exactly in the way you suggest, but I actually run a Monte Carlo simulation for the playoffs. I think those odds will be posted later today. It doesn’t show the expected upsets per round like what you might see for the NCAA basketball tournament, but in that case they are grouping teams by seed to get those numbers. In my simulations, I’m using the actual ratings.

“That might be a good test of the model?”

Any regression model is measured by minimizing the total error in the model (or actually maximizing the likelihood).

Essentially, using an initial set of ratings, a likelihood for each game is calculated and then the likelihood for all the games is totaled. For example, if Team A, Team B, and Team C start with the same initial rating, then a 42-0 victory by Team A would be unlikely but a 21-21 tie between Team B and Team C would be highly likely. If there can be any change to the ratings that increase the total likelihood of the results seen on the field, then the program makes those changes and repeats the process of totaling the likelihood again and making further changes to increase the likelihood. This is repeated until there are no more changes that can be made to the ratings that will significantly increase the likelihood. Using the example again, after a series of changes we would naturally expect Team A to end up 42 points better than Team B, who would naturally have the same rating as Team C. However, things get more complicated if Team A plays Team C and only wins 28-14, since now it is not possible to come up with a perfect answer. However, the process generate the ratings that provide the most likely explanation for the on field results.

“Any idea what percentage of games over the entire season are predicted correctly?”

This is another number I don’t actively calculate, but I’ve sporadically checked in the past and I think I normally get around 80-85% correct for winners with the 17 point error. Although it tends to have the highest standard deviation, high school football also tends to have the highest prediction percentage since there are many more mismatches than college or the NFL. I’ve tinkered with the system enough and seen enough of others’ to know those numbers will be hard to beat on a consistent basis.

“Thank you so much for the effort and thought that went into this. And thank you for sharing it with us.It is REALLY interesting to look at some of the potential matchups between teams. Some of these games on Friday will be more fun to watch than I even imagined.”

Thanks for the kind words! I’m really grateful to Todd and the AJC for the opportunity to publish something I enjoy and to interact with people who have a sincere interest in what I’m doing. Admittedly, the ratings are far from the only way to look at GHSA teams, but I think they can provide a valuable layer of information, especially when considering I rate and rank 412 teams and 48 regions and provide projected margins for approximately 220 games on a weekly basis, plus generate playoff odds for 176 teams through five rounds of the playoffs. It’s simply too much data for anyone to truly do a deep player-by-player and coach-by-coach analysis for.

Loren Maxwell

November 14th, 2013
4:22 am

Ok, that one is much easier to read.

RobFromNorcross

November 14th, 2013
5:37 am

Great stuff. Loren.

Suwanee 0wns

November 14th, 2013
6:35 am

Fascinating! Thank you so much for sharing your methodologies and offering some insights. And again, thank you for sharing the results of your model and analysis.

I assume “logistic” means that you are taking the “natural log” of the variable and feeding it into your model rather than the raw variable?

I have absolutely never attempted to build a sports model. I was taught when building predictive models to divide the data into 3 parts. The first third is used to mine the data to find significant variables. The second third is used to build the actual equations and the coefficients of the variables. And the final third is used to check the model for reasonableness and to create estimates of predictive accuracy.

I can tell you that FICO score models are built like that although I have never worked with Fair Isaacs. Perhaps you have a career with them if you are ever interested.

Regarding the data mining step, there is little need since you are dealing with a single variable – final score. But it might be interesting to see if there are other significant variables such as: years of experience for the head coach, number of returning seniors, and etc.

In particular it seems to me that Region may play a role beyond simply strength of schedule. Most of us recognize that Region 1 of AAAAAA has teams with very strong defenses compared to other regions. That would have a tendency to depress the overal rating number for a team in Region 1. If the same team played most of their games against Region 2 teams, then they would have a higher rating number. Therefore in games between regions (such as the playoffs) the scores of Region 2 teams would be slightly overstated compared to Region 1 teams. A variable for something like “Region Defensive Strength” might offer more accurate predictions just as “home game” creates greater accuracy.

I remember your “Monte Carlos” and they are always very interesting. It is amazing that one statistician got so much attention (I forget his name) for simply running a Monte Carlo state by state to predict the last presidential election results. Perhaps AJC/Cox should consider retaining your services at election time.

Thank you so much Mr. Maxwell – both for your model results, and for sharing your insights. It is absolutely fascinating. And it adds another fun element to the already exciting High School football in Georgia.

BTW, don’t you ever sleep?

GwinnettDad

November 14th, 2013
6:59 am

@Sportsnut: @Sprotsknot- You just saw Loren comment on this on the other thread but you obviously have trouble with the English language, These rankings are based only on actual games played this year and are not based on what the preseason “beliefs” were. At this point in the season there are enough games played giving actual results that there is no need to make any estimates about team strength. These are the computer models best estimate to take all the games played THIS YEAR. Thank you RobfromNorcross

Clearly Region 2 was the weakest region, and we’ll see that tomorrow night. I gave you other evidence, but your prejudices and know-it-all-isms have been just too much.

Loren Maxwell

November 14th, 2013
8:13 am

@RobFromNorcross: “Great stuff. Loren.”

Thanks, and as always, I appreciate the interest and support!

@Suwanee 0wns:
“Thank you so much for sharing your methodologies and offering some insights. And again, thank you for sharing the results of your model and analysis.”

You’re very welcome. Again, I appreciate the interest and support.

“I assume ‘logistic’ means that you are taking the ‘natural log’ of the variable and feeding it into your model rather than the raw variable?”

Logistic regression uses what’s called the “logistic curve”. Wikipedia actually gives a good explanation for it, but it basically allows the model to take a final margin of victory and convert it into a winning percentage for each game. I don’t have the numbers I use in front of me, but imagine a tie being 0.500 of a win, a one point victory actually being something like 0.650 of a win, a two point victory being 0.665, three points being 0.675, seven points 0.700, 14 points 0.775, 35 points 0.965, etc. Clearly the first point of a victory is worth the most and each one afterward contributes some but on a diminishing basis. It fits the data much better than a linear model.

“I have absolutely never attempted to build a sports model.”
Good . . . I don’t need the competition! 

“Regarding the data mining step, there is little need since you are dealing with a single variable – final score. But it might be interesting to see if there are other significant variables such as: years of experience for the head coach, number of returning seniors, and etc.”

I also use the site of the game and past performance for the preseason and early season ratings, but the other data is either too difficult to gather or seems to me to be too difficult to quantify accurately at the high school level. When Lincoln County loses Larry Campbell to retirement, I’m sure it’ll be noticed, but not all schools place the same emphasis on their football programs. I could see this as more available and easier to quantify in the NFL, where all the incentives are practically identical – that is, win.

“In particular it seems to me that Region may play a role beyond simply strength of schedule . : . A variable for something like ‘Region Defensive Strength’ might offer more accurate predictions just as “home game” creates greater accuracy.”

In general, more variables will always appear to add more accuracy to a model (even if they don’t), but the results might be interesting. To know for sure, you’d have to measure p-values and do other calculations that I’d have to refresh myself on. It’s been a while for some of this stuff, even for me!

“Thank you so much Mr. Maxwell – both for your model results, and for sharing your insights.”

Again, no problem (and you’re welcome to call me Loren!).

“BTW, don’t you ever sleep?”

Looks like as much as you do!

Scott

November 14th, 2013
8:23 am

Being the data nut that I am, I automatically scanned the list to find out who was ranked #412. Could only find #411. Am I overlooking something?

BPH

November 14th, 2013
8:30 am

Loren and also SuWanee, thank you both for the detail and information “ask and answered” here on Loren’s system. This is the most “detail” I’ve seen on the Loren’s model since I’ve followed it for about 2 years along with Ned Freeman’s (CalPreps) ratings and predictions. A couple of Comments. First I’d note that in Loren’s regular season predictions there seems to be a 2 point “home field” kicker. This “kicker” seems to be added whether the game is in a different community’s stadium 60 miles away or in a school system’s “shared” municipal stadium equidistance from the competing teams. Loren, Do you EVER manually “correct” within your model for ANY factor/s? Will a “home field” kicker be added to the home team in the Dome? Also, I might note that Freeman seems to “attempt” some early season “corrections” in his model that are gradually filtered out as the season progresses. Loren, do you attempt anything like this also??

Finally I would simply note this: It Blows me away, Loren, that with all the time you’ve obviously put into this tremendous effort over the years of designing a model to rate and predict GHSA teams and to a degree trying to determine the level of “competitiveness” we may expect to see in any given matchup……. That…. you don’t bother to then “TRACK” the historical “results” of your effort.

80-85%??? …. are you kiddin’ me??? Wouldn’t it be fairly easy to “finish” this Great project by going ahead and measuring the EXACT results weekly and over time? I’d think the “competitive” nature alone of your effort in general would dictate this finishing touch. I’d also think it would be an incredibly easy calculation requiring next to no time with very little “processor” drain. Is this wrong?? Spending the time and effort to design and implement such a model …. but then failing to accurately “track” it’s success and failures TO THE Nth DEGREE …. honestly… befuddles me.

I do love this stuff, though. I’m a fan Loren. I Love your work and also the efforts of Todd and Chip over at GHSFD that make reading and following this stuff possible. Thank You. BTW – props to SuWanee for his questions that help shed some light on “The Maxwell” ratings as well.

Scott

November 14th, 2013
8:54 am

Missing Data – Class A – 66th ranked team?

Todd Holcomb

November 14th, 2013
9:09 am

I thought of another way to explain why a 9-1 team playing a tougher schedule might be ranked below a 9-1 team with a lesser schedule. SOS is generally based on the average rating of your opponents. But let’s say you play the best five teams in the state and the worst five teams in the state. That would be a lesser schedule than if you played teams that are ranked 170-179. But for a good team, which is the tougher schedule? The #1 team in the state has an pretty good change of going 10-0 vs. teams 170-179 (see below), but will have a much harder time going 10-0 vs. #1-5 and #408-412. And let’s say the team playing that latter schedule blows out all of those top five teams?

So playing the ”better” schedule should not always determine the higher-ranked team when W-L records are the same.

Teams 170-79:

Calvary Day
Prince Avenue Christian
Duluth
Pebblebrook
Dawson County
Jonesboro
Landmark Christian
Irwin County
Cedar Shoals
Villa Rica

Loren Maxwell

November 14th, 2013
9:17 am

@Scott: “Being the data nut that I am, I automatically scanned the list to find out who was ranked #412. Could only find #411. Am I overlooking something?”

The 412th team is KIPP Collegiate, who is not actually part of any region so it doesn’t end up getting listed in this region-by-region format.

They are ranked 398th with a 1-2 record, a rating of -10.99, and a schedule strength of 15.62.

Todd Holcomb

November 14th, 2013
9:18 am

”Missing Data – Class A – 66th ranked team?”

Scott – Wondered if anybody would ever catch that. :) Loren sends me the rankings, and I format them and publish. In doing so, I deleted two Class A schools that are not playing full varsity schedules – #66 KIPP Academy and #75 Providence Christian.

Not sure of my rationale for not including them, but that’s what happened.

Scott

November 14th, 2013
9:25 am

Loren Maxwell and Todd Holcomb – thanks for clearing that up! Appreciate it.

WolfDawg

November 14th, 2013
9:30 am

Being an IT guy, I like all these blathering numbers….

BUT I LOVE the fact that the games will be decided on the gridiron..

Max now makes it “Las Vegas” style gambling, if there’s a forum for it with the “points spread”

34 hours till the real games are decided where they should be…… On the FIELD!!

BTW – 7 AAAAAA pulls out the BROOM in round 1

Loren Maxwell

November 14th, 2013
9:41 am

@BPH: “Loren, Do you EVER manually ‘correct’ within your model for ANY factor/s? Will a ‘home field’ kicker be added to the home team in the Dome?”

I never manually correct for anything and just use the straight score and the site as pulled off of GHSFHA. I’ve designed the system to use a home advantage in terms of either full home, half home, neutral, half away, or full away, but in reality only the full home, neutral, and full away are ever used. The home advantage is recalculated each week just as with the team ratings. Currently it is 1.01, but it usually is a little higher than that.

“Also, I might note that Freeman seems to ‘attempt’ some early season ‘corrections’ in his model that are gradually filtered out as the season progresses. Loren, do you attempt anything like this also??”

I don’t attempt to make any corrections in the way that you’ve described from Freeman’s system (although I’m not familiar with what he does). The only portion that might come close to that is that I use some basic adjustments for new teams with no history. Basically, if a team is new, I assume they start with a ratings that would win 10% of games if they played every team in their classification. It places new teams in the bottom end of their classification, which historically this seems about right. However, for those teams the preseason ratings drop out much faster so that initial doesn’t handicap them if they’re quickly proving they are better than that.

“Finally I would simply note this: It Blows me away, Loren, that with all the time you’ve obviously put into this tremendous effort over the years of designing a model to rate and predict GHSA teams and to a degree trying to determine the level of ‘competitiveness’ we may expect to see in any given matchup……. That…. you don’t bother to then ‘TRACK’ the historical ‘results’ of your effort.”

Perhaps I shorted myself in my explanation!

I don’t actively track the ~predications~ of the system, but I do actively track the ~retrodictions~ of the system to see how well the ratings fit the results on the field (Note: retrodictions is a made up word used by people like me to describe this type of tracking).

For example, of the 2,024 GHSA-only games (does not include out of state competition), the ratings as they are today accurately portrays 1,864 of the winners (accounting for home advantage). In other words, if you used the ratings as of today and made the predictions retroactively, the system would show 1,864 of the games with the correct winner. The system missed 155 games (the system would consider these upsets) and there were 5 ties, for a total of 92.2% retrodiction accuracy.

I also track the retrodictive standard error of the margins. Using the “raw” difference between teams, the standard error is 12.98 points. Using the adjusted margins, the standard error is 12.01 points. For anyone not familiar with standard error, basically it states that approximately 84.1% of the actual scores will fall within the 12.01 points of the adjusted margin calculated by the ratings.

However, I don’t actively track the predictions percentage and the prediction standard error, although you’re right, it would yield some great numbers. I’ll try to crank out this season’s results sometime today to share.

Loren Maxwell

November 14th, 2013
9:47 am

Opps, fat-fingered some numbers. My above post should have said:

For anyone not familiar with standard error, basically it states that approximately *68.3%* of the actual scores will fall within the 12.01 points of the adjusted margin calculated by the ratings.

Todd Holcomb

November 14th, 2013
10:19 am

Loren Maxwell

November 14th, 2013
11:03 am

@Suwanee 0wns and @BPH:

OK, here are how the ratings have done week to week looking forward (hopefully the format will work out correctly):


Week Right Wrong Tie Pct StError AdjMargin
1 33 14 1 69.8% 20.62
2 129 44 1 74.4% 19.75
3 138 39 2 77.7% 18.57
4 140 40 0 77.8% 18.80
5 138 31 0 81.7% 17.66
6 144 33 1 81.2% 16.02
7 137 25 0 84.6% 14.89
8 137 31 0 81.5% 15.57
9 165 26 0 86.4% 14.55
10 166 25 0 86.9% 14.17
11 152 38 0 80.0% 14.60
12 156 39 0 80.0% 15.99

Season 1635 385 5 80.9% 16.63

This shows that over the whole season, the projected margins have picked the correct winner 80.9% of the time and 68.3% of the projected margins are within 16.63 points.

As might be expected, both numbers tend to improve as the season progresses (percent correct gets higher while standard error get lower).

Loren Maxwell

November 14th, 2013
11:06 am

Here’s another try at the formatting:


Week     Right   Wrong   Tie     Pct     StError AdjMargin
   1        33      14     1    69.8%                20.62
   2       129      44     1    74.4%                19.75
   3       138      39     2    77.7%                18.57
   4       140      40     0    77.8%                18.80
   5       138      31     0    81.7%                17.66
   6       144      33     1    81.2%                16.02
   7       137      25     0    84.6%                14.89
   8       137      31     0    81.5%                15.57
   9       165      26     0    86.4%                14.55
  10       166      25     0    86.9%                14.17
  11       152      38     0    80.0%                14.60
  12       156      39     0    80.0%                15.99

Season    1635     385     5    80.9%                16.63

Loren Maxwell

November 14th, 2013
11:07 am

Formatting success!

RobFromNorcross

November 14th, 2013
11:37 am

Loren, might be too much work but do you have these prediction success numbers for just AAAAAA games?

Loren Maxwell

November 14th, 2013
12:10 pm

@RobFromNorcross: “Loren, might be too much work but do you have these prediction success numbers for just AAAAAA games?”

Here’s for each division:

AAAAAA: 270-83-1 = 76.4%, 14.92 spread error
AAAAA: 367-75-1 = 83.0%, 15.47 spread error
AAAA: 370-98-1 = 79.0%, 15.32 spread error
AAA: 321-66-1 = 82.9%, 15.74 spread error
AA: 318-68-2 = 82.2%, 14.76 spread error
A: 319-89-2 = 78.0%, 18.06 spread error

I’m slightly surprised. Although it’s not huge, I didn’t think there’d be as much difference between classifications.

Sportsnut

November 14th, 2013
12:55 pm

Fascinating.

Sportsnut

November 14th, 2013
1:15 pm

All this is pretty interesting and informative. The issue I have is at the end of the season, a team will have a certain rating. When the new year starts, that rating follows them. The system, as was told, takes into account past success historically. What happens when that team underachieves as related to their previous rating?. The system is designed to rely less on the past as the year goes, to the point of not counting on the previous rating by current seasons end. They did not perform up to expectations and their rating shows no change to reflect that. This year, region 8 was not what their rating would suggest. Could this situation cause the regions rating to drop? I don’t think anyone saw this coming for region 8 but things happen that the computer does not and can not include in it’s evaluation. That is why I said Humans program computers. If what you put in is flawed, what comes out will be also. Coaching changes, new systems, different players, school closing, new ones opening. Any number of things can change that are not included in the assessment, the initial assessment.

RobFromNorcross

November 14th, 2013
2:06 pm

Loren,

Your comments on the retrodiction accuracy are interesting (to a statistics geek like me) and may provide some insights into the use of the model as a prediction tool.

Since you are trying to program the computer to produce an accurate ranking of team strength, then you have chosen to use as inputs 1) who played, 2) what was the score and 3) where was it played.

Of course we all know that things happen that cannot be explained with only this limited information. The fact that North Gwinnett beat Norcross by 19, then Norcross beat Collins Hill by 28, then Collins Hill beat North Gwinnett by 25 is an example of such a situation. There is no way that the computer could predict such outcomes unless there were other variables at play that could account for the difference. Your retrodiction success percent of 92% says that even taking into account after-the-fact information, there are still 8% of the games that cannot be predicted. They are outliers… no way to predict them.

If you add in the fact that the teams are evolving which means the most accurate model is changing, (some of the previous outliers each week are being changed) then something in range of 80% prediction rate is probably the best that can be achieved.

Playing the games is more exciting than reading numbers, but it is interesting to get some understanding of how predictable the game is (and how unpredictable)

Can’t wait till Friday night, only 30 hours away!

Cool_Coach1

November 14th, 2013
2:08 pm

The bottom line for all you people that aren’t into all this “cyphering” is that 80% of the time this formula seems to be correct. If I’m a betting man (not on High School sports of course) those numbers are pretty dang good! If you’re trading stocks, running a business etc. I like those odds. Hat’s off to Loren and Todd… the people that understand what goes into giving us this insight want you to know your efforts are appreciated! The ones who don’t like “numbers” can watch their teams from the couch during the “money” rounds of the playoffs!

BPH

November 14th, 2013
4:53 pm

Thanks so much Loren for the follow up week by week numbers. EXACTLY what I was hoping to see! I must confess I read your first response at least 4 times. Then when you got to the “Retrodiction” part, I had to carefully analize(note: Analize is a made up word for people like me that agonize over an analysis clearly beyond our comprehension) by rereading it twice more. Regardless, I think I’m on the right track now. I do love this stuff! Thanks again and keep up the Great work.

Sportsnut

November 14th, 2013
4:54 pm

Good luck to all and to all safe travels. I hope the kids are ready and able to back up all this talk about what they are going to do.