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San Diego

Also known as: San Diego
Program History

Model Outputs

2025-2026 latest available
Catalog

No materialized model snapshot for 2015 yet, so this section is showing the latest available team-model rows.

Output is shown as model rating with league rank in parentheses when available.

Model Output Notes
Elo Elo Streaming paired-comparison rating with recency baked into sequential updates. More → 735 (#529) -
Bradley-Terry Bradley-Terry Static logistic paired-comparison model with one team strength parameter. More → 778 (#509) -
Bradley-Terry Recency Bradley-Terry Recency Static Bradley-Terry with exponential recency weights on newer games. HCA +75 elo More → 858 (#642) HCA +75 elo
Margin Margin Linear team-strength model fit on point differential instead of binary wins. HCA +2.8 More → -1.4 (#186) HCA +2.8
Margin Recency Margin Recency Margin regression with exponential recency weights on newer games. HCA +3.7 More → -6.8 (#467) HCA +3.7
Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → 1.000 (#9) -
Pythagorean Pythagorean Pythagorean win expectation from raw points scored and allowed. More → 0.100 (#537) -
Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. AdjNet +55.7 More → 0.999 (#18) AdjNet +55.7
Adjusted Efficiency Adjusted Efficiency Opponent-adjusted efficiency model with separate offensive and defensive components. AdjNet -11.2 More → 0.216 (#511) AdjNet -11.2
Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. AdjNet +55.2 More → 0.999 (#18) AdjNet +55.2
Log Adjusted Log Adjusted Log-scale adjusted efficiency model that downweights blowout leverage. AdjNet -11.0 More → 0.214 (#518) AdjNet -11.0
Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. AdjO 75.4 | AdjD 45.8 More → 0.937 (#49) AdjO 75.4 | AdjD 45.8
Points Off/Def Points Off/Def Raw points regression with separate offensive and defensive team parameters. AdjO 62.0 | AdjD 69.3 More → 0.341 (#516) AdjO 62.0 | AdjD 69.3
Points Off/Def Recency Points Off/Def Recency Off/def points regression with exponential recency weights. AdjO 61.7 | AdjD 66.6 More → 0.390 (#533) AdjO 61.7 | AdjD 66.6
Core Ensemble Core Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and points off/def. Blend of Elo, BT, Margin, PythLog, PtsOD More → 0.344 (#492) Blend of Elo, BT, Margin, PythLog, PtsOD
Recency Ensemble Recency Ensemble Equal-logit blend of Elo, recency BT, recency margin, log-adjusted pyth, and recency points off/def. Blend of Elo, BT, Margin, PythLog, PtsOD More → 0.333 (#507) Blend of Elo, BT, Margin, PythLog, PtsOD
Dynamic Bradley-Terry Dynamic Bradley-Terry Time-evolving paired-comparison model with latent team strength drift. RD 95 | GP 25 More → 794 (#472) RD 95 | GP 25

2015 Schedule & Results

Date Vs/At Opponent Result Score
2014-11-16 @ Santa Clara L 0 - 0
2014-11-20 vs MSU Moorhead - Preview
2014-11-22 @ Notre Dame de Namur L 46 - 77
2014-11-25 @ Western Wash. L 47 - 76
2014-11-26 @ Seattle Pacific L 54 - 84
2014-12-05 vs Cal Poly Humboldt L 57 - 73
2014-12-06 vs CSUSB L 53 - 64
2014-12-12 vs Sonoma St. L 59 - 66
2014-12-21 @ Sonoma St. L 64 - 70
2015-01-02 vs Cal St. East Bay L 51 - 69
2015-01-03 vs Cal St. Monterey Bay L 80 - 84
2015-01-09 @ Cal Poly Pomona - Preview
2015-01-10 @ UC San Diego - Preview
2015-01-16 @ Chico St. - Preview
2015-01-17 @ Stanislaus St. - Preview
2015-01-23 vs Cal St. Dom. Hills - Preview
2015-01-24 vs Cal State LA - Preview
2015-01-29 @ CSUSB L 53 - 80
2015-01-31 @ Cal Poly Humboldt L 61 - 85
2015-02-06 @ Cal St. Monterey Bay L 45 - 67
2015-02-07 @ Cal St. East Bay L 55 - 67
2015-02-13 vs UC San Diego - Preview
2015-02-14 vs Cal Poly Pomona - Preview
2015-02-20 vs Stanislaus St. L 55 - 65
2015-02-21 vs Chico St. - Preview
2015-02-27 @ Cal State LA - Preview
2015-02-28 @ Cal St. Dom. Hills - Preview

2015 Roster

No roster data available for this season.