Sports from the Basement

Record8837−7214−7055.1% of 16051 decided · 16121 settled

Return on investment−0.96%staked at the recommended bet size

Worst stretch−60.28%Fri 2 Oct, 7 bets

Scope

Bets rated 4 or 5, one per side, at the Consensus price — the average across the books we cover. Sun 30 Aug – Thu 15 Oct. Pools every rating rule run so far; "Current rule" shows the one in use today.

Daily ROI20 losing days of 45

SegmentW−L−DHitROIN

Moneyline207−124−062.5%—331

Spread174−212−445.1%—390

Total183−174−651.3%—363

Props8273−6704−6055.2%−0.59%15037

How these are priced and staked. Each bet is graded at the last Consensus price before the game — close to the closing number, not the price on the board when the bet appeared — and staked at the size the board recommended: full Kelly on a 10-unit bankroll. At the first price a reader could have taken, the same stakes returned +1.45%. At one unit a bet instead, they returned −4.56%.

The settled bets

The 25 most recently graded, at the Consensus price. Every graded bet in this scope is in the download. Download this scope as CSV

Day	Match	Bet	Price	Result
Thu 15 Oct	Calgary Flames at Vegas Golden KnightsNHL	Raphael Lavoie U 1.5Player Shots on Goal	−240	won
Thu 15 Oct	Calgary Flames at Vegas Golden KnightsNHL	Trevor Connelly O 1.5Player Shots on Goal	−182	lost
Thu 15 Oct	Calgary Flames at Vegas Golden KnightsNHL	Mitch Marner U 0.5Player Power Play Points	−177	lost
Thu 15 Oct	Calgary Flames at Vegas Golden KnightsNHL	Trevor Connelly Trevor Connelly2+ Shots on Goal	−182	lost
Thu 15 Oct	Calgary Flames at Vegas Golden KnightsNHL	Dylan Coghlan U 1.5Player Shots on Goal	−185	lost
Thu 15 Oct	Calgary Flames at Vegas Golden KnightsNHL	Tomas Hertl U 0.5Player Power Play Points	−237	won
Thu 15 Oct	Utah Mammoth at Edmonton OilersNHL	Vasily Podkolzin U 0.5Player Power Play Points	−275	won
Thu 15 Oct	St. Louis Blues at Minnesota WildNHL	Colton Parayko U 0.5Player Assists	−330	lost
Thu 15 Oct	St. Louis Blues at Minnesota WildNHL	Bobby Brink U 0.5Player Assists	−370	won
Thu 15 Oct	Milwaukee Brewers at Los Angeles DodgersMLB	Max Muncy U 0.5Batter Runs	−148	won
Thu 15 Oct	Milwaukee Brewers at Los Angeles DodgersMLB	Will Smith U 0.5Batter Runs	−160	won
Thu 15 Oct	Milwaukee Brewers at Los Angeles DodgersMLB	Max Muncy U 0.5Batter Walks	−157	won
Thu 15 Oct	Winnipeg Jets at Chicago BlackhawksNHL	Haydn Fleury U 1.5Player Shots on Goal	−235	won
Thu 15 Oct	Winnipeg Jets at Chicago BlackhawksNHL	Oliver Moore U 1.5Player Shots on Goal	−177	lost
Thu 15 Oct	Winnipeg Jets at Chicago BlackhawksNHL	Kyle Connor U 0.5Player Power Play Points	−275	lost
Thu 15 Oct	St. Louis Blues at Minnesota WildNHL	Matt Kiersted U 0.5Player Total Points	−265	won
Thu 15 Oct	St. Louis Blues at Minnesota WildNHL	David Spacek U 0.5Player Total Points	−278	won
Thu 15 Oct	St. Louis Blues at Minnesota WildNHL	Danila Yurov U 1.5Player Shots on Goal	−195	lost
Thu 15 Oct	St. Louis Blues at Minnesota WildNHL	Adam Jiricek O 1.5Player Shots on Goal	−135	lost
Thu 15 Oct	St. Louis Blues at Minnesota WildNHL	Adam Jiricek Adam Jiricek2+ Shots on Goal	−135	lost
Thu 15 Oct	Milwaukee Brewers at Los Angeles DodgersMLB	Max Muncy U 0.5Batter Singles	−240	lost
Thu 15 Oct	Milwaukee Brewers at Los Angeles DodgersMLB	Shane Drohan O 0.5Pitcher Total Walks Allowed	−201	won
Thu 15 Oct	Milwaukee Brewers at Los Angeles DodgersMLB	Shane Drohan Shane Drohan1+ Walks Allowed	−201	won
Thu 15 Oct	Philadelphia Flyers at Detroit Red WingsNHL	David Jiricek U 0.5Player Total Points	−240	won
Thu 15 Oct	Philadelphia Flyers at Detroit Red WingsNHL	Nate Danielson U 0.5Player Total Points	−205	lost

What the model claimed, and what happened

Fri 16 Oct · 197,807 settled predictions

33.4%claimed, averaged

34.7%actually happened

0.1535Brier score, lower is better

+0.323skill against a constant guess

Close to honest on average — 33.4% claimed against 34.7% observed. That average hides the finding below it.

The further we depart from the market, the more we overstate

Every prediction, grouped by the rating the model gave it. Low ratings track the price closely and are calibrated; the strongest depart furthest, and that is where the claim and the outcome come apart.

RatingClaimedActualGapGap, to scalen

Every prediction33.4%34.7%−1.3197,807

Rated 020.1%21.5%−1.4143,063

Rated 167.9%69.1%−1.251,826

Rated 379.2%75.8%+3.42,211

Rated 464.1%64.1%+0.1593

Rated 568.1%77.2%−9.1114

Why a rating is capped rather than trusted at the top of its range, and why this site publishes no return by rating: the ordering of these buckets is stable, their levels are not.

Calibration, market by market

Claimed probability along the bottom, the fitted correction up the side; the faint diagonal is perfect calibration. 18 markets carry a fitted curve — the rest are measured but not yet fitted, and are in the table below rather than dropped.

Batter hitsn = 46,406

claimed →

Batter total basesn = 46,099

claimed →

receiving_yardsn = 24,978

claimed →

Batter doublesn = 15,824

claimed →

Batter RBIn = 15,465

claimed →

offense_receptionsn = 13,230

claimed →

rushing_yardsn = 12,439

claimed →

offense_shotsn = 8,998

claimed →

Pitcher strikeoutsn = 6,791

claimed →

passing_yardsn = 5,452

claimed →

passing_touchdownsn = 3,340

claimed →

rushing_attemptsn = 2,875

claimed →

shotsn = 2,155

claimed →

team_totaln = 1,736

claimed →

Pitcher earned runsn = 1,374

claimed →

Pitcher hits allowedn = 1,338

claimed →

shots_on_targetn = 1,323

claimed →

defense_savesn = 446

claimed →

Where we are worst

Every market we publish a board for, sorted by skill — the Brier score against a constant guess at the same base rate. Below zero the model lost to that guess: 15 of 75 markets. Nothing is removed and nothing is sorted flatteringly.

25 sit on fewer than 500 settled predictions. A skill score swings hard on a small sample, so those rows follow the rest rather than leading them — reported, not ranked.

MarketClaimedActualBrierSkilln

team_totalNCAA FB47.2%50.0%0.2564−0.0261,200

TotalNCAA FB47.6%50.0%0.2525−0.010794

SpreadNCAA FB47.2%50.0%0.2523−0.009788

Pitcher earned runsMLB48.2%50.5%0.2518−0.007560

Pitcher hits allowedMLB48.3%50.0%0.2506−0.002536

rushing_attemptsNCAA FB46.8%50.0%0.2491+0.004706

Pitcher outsMLB47.2%50.0%0.2481+0.007552

horse_exacta_baseline2.0%2.0%0.0196+0.008142,192

horse_win_baseline13.6%13.6%0.1164+0.01221,308

horse_exacta2.0%2.0%0.0195+0.013142,192

horse_exacta_box_baseline4.0%4.0%0.0381+0.01771,125

horse_exacta_box4.0%4.0%0.0377+0.02871,125

horse_place_baseline27.1%27.1%0.1922+0.02821,241

Batter singlesMLB49.0%49.8%0.2393+0.0436,571

horse_show_baseline40.1%40.1%0.2285+0.04920,904

Racing · win13.2%13.6%0.1118+0.05121,308

Racing · place25.0%27.1%0.1839+0.07021,241

Racing · show37.0%40.1%0.2222+0.07520,904

passing_attemptsNCAA FB35.2%38.9%0.2120+0.108855

Batter total basesMLB25.6%28.5%0.1771+0.13218,530

passing_attemptsNFL35.6%38.8%0.1996+0.159655

offense_receptionsNCAA FB35.5%38.4%0.1973+0.1664,197

offense_touchdownsNCAA FB12.1%13.2%0.0955+0.1662,681

offense_pointsNHL40.5%40.7%0.1978+0.1816,875

horse_all_baseline8.2%8.2%0.0609+0.195276,770

Batter RBIMLB48.6%49.4%0.1982+0.2076,270

rushing_yardsNCAA FB32.4%34.0%0.1775+0.2095,234

receiving_yardsNCAA FB29.3%30.9%0.1682+0.2138,899

Racing · all pools7.8%8.2%0.0592+0.217276,770

rushing_attemptsNFL42.0%43.8%0.1912+0.2231,401

offense_touchdownsNFL8.7%9.7%0.0668+0.2373,436

passing_touchdownsNCAA FB40.1%42.4%0.1846+0.2441,618

passing_yardsNCAA FB36.3%40.1%0.1814+0.2452,637

offense_receptionsNFL36.1%37.7%0.1729+0.2645,790

Batter hitsMLB30.6%31.2%0.1572+0.26818,699

Pitcher strikeoutsMLB37.0%37.8%0.1714+0.2712,617

offense_shotsNHL42.0%43.0%0.1774+0.2768,998

receiving_yardsNFL27.2%28.3%0.1446+0.2888,497

offense_assistsNHL37.7%38.9%0.1654+0.3045,199

MoneylineNCAA FB49.0%49.9%0.1729+0.308727

rushing_yardsNFL34.0%33.5%0.1532+0.3123,172

passing_touchdownsNFL39.3%40.0%0.1649+0.313717

passing_yardsNFL40.2%42.8%0.1673+0.3171,049

Batter runsMLB27.0%27.4%0.1319+0.33712,086

offense_powerplay_pointsNHL42.3%43.6%0.1376+0.4412,703

offense_goalsNHL32.0%32.4%0.1064+0.5148,363

Batter doublesMLB48.8%49.5%0.1154+0.5386,522

Batter home runsMLB27.1%27.2%0.0724+0.63512,452

Batter stolen basesMLB32.9%32.9%0.0621+0.7197,885

Batter triplesMLB27.4%27.5%0.0194+0.9023,935

rushing_touchdownsNFL42.7%58.3%0.3113−0.28112

batter_strikeoutsMLB46.8%50.0%0.2961−0.18418

SpreadNBA42.4%50.0%0.2889−0.15510

MoneylineNBA47.2%50.0%0.2800−0.12010

TotalNHL47.6%50.0%0.2663−0.065264

passing_completionsNCAA FB46.3%50.0%0.2609−0.043364

TotalNFL48.8%50.0%0.2557−0.023116

passing_interceptionsNFL49.1%50.0%0.2534−0.014238

passing_completionsNFL48.7%50.0%0.2523−0.009232

team_totalNFL49.0%50.0%0.2511−0.005222

TotalMLB48.5%50.0%0.2500+0.000366

SpreadNFL48.1%50.0%0.2453+0.019114

SpreadMLB47.4%49.3%0.2450+0.020361

team_totalNHL48.2%50.0%0.2447+0.021312

passing_interceptionsNCAA FB47.1%50.0%0.2412+0.035114

SpreadNHL48.0%49.4%0.2396+0.042263

MoneylineNFL48.9%50.0%0.2373+0.051110

defense_blocksNHL50.7%53.9%0.2351+0.054412

MoneylineNHL47.9%50.2%0.2337+0.065265

MoneylineMLB48.2%50.0%0.2326+0.070350

batter_strike_outsMLB50.2%58.1%0.2238+0.080117

defense_savesNHL36.2%40.4%0.2043+0.151446

pitcher_walksMLB53.4%57.4%0.2028+0.17047

TotalNBA46.7%50.0%0.2021+0.19210

batter_walksMLB44.1%43.4%0.1854+0.245387

When the model changed

Every day the blend’s source weights were rewritten. A record is only meaningful against a model that was not quietly swapped underneath it.

DayWeightsBoardsSources

Fri 16 Oct3731124

Mon 14 Sep211

Fri 28 Aug412

Tue 12 May9129

Mon 6 Apr412

Mon 9 Feb5118

Sun 18 Jan1914