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Model Accuracy — NFL

How well do ranking models predict regular-season game outcomes?

Pick Accuracy

Fraction of games where the predicted favourite won

higher is better

Brier Score

Mean squared error of probability predictions (lower is better)

lower is better

Log Loss

Cross-entropy loss of probability predictions (lower is better)

lower is better

Results by Season

Season Games Pick Accuracy Brier Score Log Loss
2025 271 63.5% 0.2262 0.6432
2024 272 68.8% 0.2132 0.6145
2023 272 59.9% 0.2342 0.6625
2022 269 62.5% 0.2259 0.6414
2021 271 58.3% 0.2344 0.6626
2020 255 66.3% 0.2176 0.6286
2019 255 64.3% 0.2232 0.6392
2018 254 63.0% 0.2214 0.6308
2017 256 64.8% 0.2164 0.6210
2016 254 64.2% 0.2205 0.6290
2015 256 65.2% 0.2258 0.6460
2014 255 68.6% 0.2069 0.6003
2013 255 65.5% 0.2164 0.6200
2012 255 62.0% 0.2186 0.6242
2011 256 64.1% 0.2109 0.6072
2010 256 62.5% 0.2319 0.6555
2009 256 69.5% 0.2042 0.5949
2008 255 64.7% 0.2161 0.6226
2007 256 65.2% 0.2128 0.6111
2006 256 57.4% 0.2376 0.6660
2005 256 67.6% 0.2106 0.6083
2004 256 64.1% 0.2237 0.6378
2003 256 64.1% 0.2207 0.6323
2002 255 59.6% 0.2273 0.6452
2001 248 65.3% 0.2288 0.6529
2000 248 62.9% 0.2223 0.6315
1999 248 62.5% 0.2223 0.6338

Calibration (All Seasons)

Each point is a 5% probability bucket. On the diagonal = perfectly calibrated. Above = model underestimates; below = overestimates.