AI Sports Prediction Leaderboard — Which AI Predicts Best? | Predicted Sports
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The AI Sports Prediction Index
Which AI predicts sports best?
GPT-5.5, GLM 5.2, Claude Opus 4.8, Gemini 3.5 Flash, Grok 4.3, DeepSeek V4 Pro, and Claude Sonnet 5 — head to head on real games. Each gets the identical line-blind data packet (no betting line, no web search), locks its call ~3 hours before first pitch, and is graded in public. No edits, no do-overs.
The honest finding so far: nobody beats the closing line consistently — not even the frontier models. The race worth watching is who gets closest.
See the standings<br>How it's scored<br>Can AI beat Vegas? →<br>Our own models' record →
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GLM 5.2
43–30
59% · Brier 0.236
🥈
DeepSeek V4 Pro
55–38
59% · Brier 0.242
🥉
GPT-5.5
63–42
60% · Brier 0.243
MLB standings · ranked by Brier (lower = better)
105 graded games
Model<br>Record<br>AccAccuracy<br>Brier<br>ROI<br>Games
GLM 5.2👑
43–30<br>59%<br>0.236
+6.3%
73
DeepSeek V4 Pro
55–38<br>59%<br>0.242
+7.8%
93
GPT-5.5
63–42<br>60%<br>0.243
+8.8%
105
Claude Opus 4.8
57–48<br>54%<br>0.244
-2.0%
105
Claude Sonnet 5
52–33<br>61%<br>0.246
+12.6%
85
Gemini 3.5 Flash
57–48<br>54%<br>0.246
-2.7%
105
Grok 4.3
56–49<br>53%<br>0.250
-4.7%
105
Pick the favoritebaseline
62–41<br>60%<br>0.398
+7.0%
103
Pick the home teambaseline
48–57<br>46%<br>0.543
-14.9%
105
Brier = mean squared error of the win probability (0 = perfect, 0.25 = a coin flip). Accuracy = how often the model's side won. ROI = return on a 1-unit bet on each pick at the closing market price (so beating the vig means clearing ~0%). The baselines are the bar: an AI that can't out-forecast "pick the home team" isn't forecasting. Early samples are small — read with care.
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Run totals · over/under, ranked by accuracy
Model<br>O/U acc<br>Avg miss<br>ROI<br>Totals graded
Grok 4.3<br>64%<br>±3.9
+23.0%
102
GLM 5.2<br>61%<br>±4.2
+21.1%
72
DeepSeek V4 Pro<br>60%<br>±4.0
+14.6%
90
GPT-5.5<br>58%<br>±4.0
+11.5%
102
Claude Opus 4.8<br>57%<br>±4.0
+9.1%
102
Gemini 3.5 Flash<br>55%<br>±4.0
+4.6%
102
Claude Sonnet 5<br>54%<br>±4.1
+6.3%
83
Market (closing line)baseline<br>57%
+6.6%
102
Each model projects the game's total runs (line-blind); we grade its over/under call against the closing market line (a no-vig multi-book consensus via The Odds API where captured; Kalshi before that). O/U acc = how often that call was right. Avg miss = mean runs off the actual total. ROI = return on a 1-unit bet at the closing price — so break-even is the market's price (here the favored side runs ~59¢), not 50%: a model can top 50% and still lose to the vig. The Market (closing line) row is the bar to clear. Early samples are small — read with care.
🔒 See each model's projected total on every game — not just the scoreboard.
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First inning · run in the 1st (YRFI), ranked by Brier
Model<br>YRFI acc<br>Brier<br>Graded
Gemini 3.5 Flash<br>54%<br>0.240<br>69
Claude Sonnet 5<br>54%<br>0.244<br>68
DeepSeek V4 Pro<br>59%<br>0.244<br>61
GPT-5.5<br>58%<br>0.244<br>69
Grok 4.3<br>61%<br>0.245<br>69
GLM 5.2<br>48%<br>0.249<br>69
Claude Opus 4.8<br>49%<br>0.250<br>45
Always “no run”baseline<br>44%<br>0.557<br>70
Each model calls the probability of a run in the first inning (either team), line-blind. First innings are close to a coin weighted toward "no run", so the Always "no run" baseline is the bar — an AI only shows skill by beating it on Brier. It's also the one market where our own first-inning model claims a real (modest) edge, so this table is the fairest fight on the board. Early samples are small — read with care.
UFC — best fight forecasters
Same idea, in the cage: every model gets the identical line-blind fight packet — no odds, no search — calls the winner and the method , and is graded on both. Follow the picks on every card.
No graded fights yet. Each model's locked picks grade as fights resolve — the board fills from the next card on.
How it works
Line-blind. No model ever sees the betting line. Each produces its own win probabilities and run projections from the data alone, so the board reads forecasting skill — not an echo of the market.
One packet, no search. Every model gets the same point-in-time data (ratings, Statcast, bullpens, park/umpire, situational splits) ~3h before first pitch. No web search, so it's the models we're measuring.
Graded in public, no do-overs. Each model makes one call per game and we live with it — against the outcome (accuracy, Brier) and against the closing price (ROI). Our own house models get the same treatment on the accuracy page.
Where the Index goes next
✓Live now: nightly MLB board, UFC fight board, run totals graded against the closing line, ROI at closing prices.
✓World Cup: our panel is calling every knockout match — follow the bracket.
◌The daily board update — who's hot, who's slipping, where the models disagree,...