Model Performance

All Seasons · 13,628 graded predictions

2026 2025 2024 2023 2022 All

In-sample data: 12,931 of 13,628 predictions were generated after games completed. Only the 697 prospective predictions are reliable for evaluation. Metrics will become trustworthy as the current season progresses and daily ETL generates pre-game predictions.

67%

Overall Accuracy

80%

Last 50 Games

0.2226

Brier Score (↓ better)

0.7214

Log Loss (↓ better)

0.6805

AUC (↑ better)

13,628

Predictions Graded

Calibration

When the model says X%, does the favored team win X% of the time? The vertical bar shows the predicted probability; the filled bar shows actual.

50–55%1436 games50% actual(-3pp)

55–60%1605 games54% actual(-3pp)

60–65%1680 games58% actual(-5pp)

65–70%1817 games64% actual(-3pp)

70–75%1438 games70% actual(-3pp)

75–80%1242 games75% actual(-2pp)

80–100%4410 games78% actual(-14pp)

Actual win rate Predicted

By Confidence Tier

Accuracy when the model is highly confident in its pick.

Confidence	Games	Accuracy
≥ 60%	10587	71%
≥ 65%	8907	73%
≥ 70%	7090	76%
≥ 75%	5652	77%

Score Projection Accuracy

How close were the Monte Carlo score projections to actual results? The model's own projected run differential acts as the spread.

4.8

Avg margin error (runs)

+1.0

Margin bias (under-projects)

56%

Own-line cover rate

Within 2 runs of projected margin28%

Within 5 runs of projected margin61%

Based on 13628 games with score projections. “Own-line cover rate” = how often the actual margin exceeded the model's projected margin in the predicted direction.

Top Teams by Elo

Rolling Elo rating (1500 = average). Updated after every game result.

Rank	Team	Games	Elo
1	Texas Longhorns	267	1797
2	North Carolina Tar Heels	267	1774
3	Southern Miss Golden Eagles	174	1765
4	Auburn Tigers	253	1757
5	UCLA Bruins	251	1751
6	LSU Tigers	283	1741
7	Oregon State Beavers	268	1738
8	Florida State Seminoles	253	1738
9	Florida Gators	282	1736
10	Mississippi State Bulldogs	248	1735

D1 Diamond Top 25 — 2026 Season

Composite ranking of all D1 teams with ≥5 games played.

#	Team	Record	Elo	wOBA	FIP	Run Diff	Score
1	TEXTexas Longhorns	16–0	1797	0.453	2.25	+7.6	94
2	GTGeorgia Tech Yellow Jackets	15–2	1704	0.528	2.86	+9.5	89
3	UGAGeorgia Bulldogs	15–3	1709	0.515	2.76	+8.4	87
4	TA&MTexas A&M Aggies	15–1	1726	0.468	3.18	+7.2	87
5	MSSTMississippi State Bulldogs	15–2	1735	0.467	3.22	+6.8	85
6	AUBAuburn Tigers	14–2	1757	0.401	2.28	+5.1	84
7	CLEMClemson Tigers	15–2	1734	0.437	2.77	+5.2	84
8	OUOklahoma Sooners	14–2	1734	0.433	3.10	+6.4	84
9	UNCNorth Carolina Tar Heels	15–3	1774	0.411	3.49	+5.8	83
10	MISSOle Miss Rebels	15–3	1719	0.439	2.55	+4.9	83
11	FLAFlorida Gators	15–3	1736	0.415	2.52	+4.1	82
12	FSUFlorida State Seminoles	13–3	1738	0.425	2.92	+4.6	82
13	USMSouthern Miss Golden Eagles	15–2	1765	0.402	3.33	+3.5	82
14	USCUSC Trojans	15–0	1710	0.378	2.46	+5.0	82
15	UCLAUCLA Bruins	14–2	1751	0.438	4.61	+5.8	81
16	NCSTNC State Wolfpack	14–3	1673	0.485	3.63	+7.9	80
17	LSULSU Tigers	13–5	1741	0.413	2.93	+3.9	79
18	WAKEWake Forest Demon Deacons	15–2	1705	0.445	3.90	+4.7	79
19	UKKentucky Wildcats	15–2	1692	0.411	3.37	+4.6	78
20	ALAAlabama Crimson Tide	15–3	1722	0.409	3.44	+4.1	78
21	OREOregon Ducks	13–3	1703	0.436	3.66	+5.7	78
22	UVAVirginia Cavaliers	14–3	1709	0.429	4.14	+5.1	77
23	ORSTOregon State Beavers	11–4	1738	0.375	3.06	+1.8	76
24	TENNTennessee Volunteers	13–4	1717	0.381	3.08	+3.4	76
25	UCSBUC Santa Barbara Gauchos	13–2	1682	0.398	3.36	+4.1	76

Minimum 5 games played. wOBA/FIP from FanGraphs.

How to Read This

Overall Accuracy — % of games where the model's favored team won. A coin flip is 50%; sharp sports models typically land 58–65% on college baseball.

Brier Score — measures probability calibration quality. Lower is better. A random model scores 0.25; perfect calibration scores 0.

Log Loss — penalises confident wrong predictions more harshly than Brier score. Lower is better. Random baseline is ~0.693 (ln 2); well-calibrated sports models typically land 0.55–0.62.

AUC — probability that the model ranks a random home win above a random home loss. 0.5 = no skill; 1.0 = perfect ranking. Measures discrimination independently of calibration.

Calibration chart — a perfectly calibrated model's bar would exactly touch the line in every bucket. Bars to the right mean the model is underconfident; left means overconfident.

Score projection — tracks how close the Monte Carlo run totals were to reality. “Avg margin error” is the mean absolute difference between projected and actual run differential. “Own-line cover rate” treats the model's own projected margin as the spread — above 50% means the model tends to under-project winning margins.

Elo ratings — self-correcting power ratings that update after every game. 1500 is average; top programs typically reach 1600–1700 by mid-season.