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Use SGPLab in Claude
Ask Claude about prop hit rates, correlations and lines, and get answers computed from the same data as this site.
· Set it up
Server URL: https://sgplab.com/api/mcp
In Claude (app or web), open Settings, then Connectors, choose to add a custom connector, and paste the URL (https://sgplab.com/api/mcp). Claude will ask you to sign in to SGPLab once. You can do this with a free or paid Claude account. With a free SGPLab account, you get 3 questions per day; with a Pro plan, you get unlimited.
Claude Code: claude mcp add --transport http sgplab https://sgplab.com/api/mcp
· Try asking
Examples on the Pro plan, with the answers it gave in early November 2026:
How do quarterbacks making their first career start fare against their passing prop, and is there any value in alt lines?
Badly. Since 2023, 31 quarterbacks made their first career start with a passing yards line posted. 13 went over (42%), they averaged 19 yards under the line, and the under returned +8.3% at closing prices, against −9.1% for the under in every other start.
Alt overs didn’t rescue it. Rungs near the main line lost 29%, +10 to +35 yards lost 33%, and +65 yards or more lost 40%. Only the +35 to +65 yard rungs (around +311) came close to even, at −0.9%, on 14 hits across 24 games, which is too few to call an edge.
The spread is wide: Caleb Williams threw for 93 on a 238.5 line and Jake Haener for 49 on 187.5, while Riley Leonard threw for 270 on 160.5 and Drake Maye for 243 on 164.5.
Which NBA players have the highest volume of games in which they hit an alt points line of +250 or higher, but their team doesn't cover the spread?
Since the 2024-25 season, Anthony Edwards leads with 23 games where he cleared a points line priced +250 or longer while Minnesota failed to cover, out of 40 games where he cleared one at all.
Next: OG Anunoby 22 (of 56), Tim Hardaway Jr. 21 (of 42), Julian Champagnie 21 (of 40), then Amen Thompson, Naji Marshall and Alperen Sengun with 20 each.
The rungs aren’t comparable across players. Edwards’ long hits averaged a 38-point line and Nikola Jokic’s 37, while Hardaway’s averaged 19. Prices are the consensus across books; pushes on the spread are left out.
Which NFL players are the most volatile and hit their alt stat lines most often?
Across receiving yards, rushing yards, passing yards and receptions since 2024, the shortest alt over priced +200 to +400 cleared 24.5% of the time and lost 13% at those prices (19,304 player-games).
With at least 15 games, Alec Pierce leads on receiving yards: 18 of 32 (56%) at an average of +233. Then Parker Washington, receiving yards, 47%; James Cook, rushing yards, 19 of 43 (44%); and Mack Hollins, receptions, 44% at about +298.
Most of the leaders are very volatile (standard deviation 0.6 to 0.8 of their average), which is what makes long rungs reachable. Cook is the exception at 0.46: he gets there on volume. Samples of 15 to 40 games are streaks as much as traits.
Which NBA games tonight have the most injuries/players to replace? How is the market repricing these games?
Asked on November 4: seven teams were missing a regular who was listed Out. The biggest holes were Boston without Jayson Tatum (a 27-point scorer by his recent lines, against Milwaukee) and Atlanta without Jalen Johnson (22, against Washington). New Orleans was also without Trey Murphy III, Portland without Ja Morant, and Cleveland, Denver and Minnesota were each missing a rotation player.
The market moved the points to the players next in line. In Boston, Payton Pritchard went from a recent 15.5 to 19.5, Paul George from 19.3 to 21.5 and Derrick White from 14.9 to 16.5. In Atlanta, Nickeil Alexander-Walker went from 19.3 to 21.5 and Kingston Flemings from 7.0 to 9.5.
How it played out: of the nine points lines raised by 1.5 or more on those teams, five went over and four went under. White (24), George (25) and Alexander-Walker (26) cleared; Pritchard, given the biggest bump, scored 10. Boston won by 7 as a 12.5-point favorite.
In college football, will I have a better ROI betting single digit underdogs on the moneyline, or at alt lines like -5.5?
The moneyline, by a lot. Since 2024, across 1,365 single-digit underdogs (+0.5 to +9.5), the moneyline won 37% of the time and returned −4.1% at closing prices. Dogs of +4 to +6.5 broke about even; +1 to +3.5 and +7 to +9.5 each lost about 5.6%.
At −5.5 (winning by 6 or more) the same dogs hit 24.4% of the time, which is worth about +310. Books posted that alt at a median of +238 across 1,358 games, so it returned −14.5%.
SGP Lab’s estimate for a consensus price lines up: about +196 for a +1.5 dog up to +239 for a +3.5 dog, against fair prices of +218 to +263.
· What it can and can’t do
The connector covers the NFL, NBA, NHL, MLB, WNBA and college football. Historical results are graded against real sportsbook closing lines, and answers link back to the matching page here so you can check the full table.
It only reads research data. It can’t place bets, see a sportsbook account, or build a betslip. Results describe what happened and are not picks; see the disclaimer. SGPLab is for informational and entertainment purposes only, and is intended for adults who can legally bet where they live.
A free account gets 3 deep research questions a day; a question is one burst of tool calls, so the lookups behind a single answer count once. Pro is unlimited and adds raw SQL over the full data (describe_data and query_data).
Current prices at individual sportsbooks aren’t available through the connector. Upcoming lines are consensus across books.
· Tools
| Tool | What it does |
|---|---|
slate | Upcoming games for a league that sportsbooks have priced, with start time and how many markets are posted. |
projections | Projections for players on the upcoming slate, read off the betting market: for each player and stat, a distribution fitted to every priced rung of the alternate-line ladder (consensus across books). |
lines | Current consensus lines for the upcoming slate: the median price across sportsbooks (pick'em sites excluded), only where at least two books post it. |
hit_rates | How often each player went over (or under) their prop line, graded game by game against a real book's closing line, with the record, hit rate, and the flat 1-unit profit at the actual prices. |
combo_hit_rates | How often each player cleared EVERY one of several prop lines in the same game (or stayed under all of them), graded against the consensus closing line. |
player_card | One player's recent games with the result against each night's prop line, per stat, plus a record for every stat that book priced. |
player_correlation | How often a player hit a combo where EACH leg has its own direction, e.g. points over + rebounds under, or points over + his team fails to cover. |
team_correlation | How often each TEAM hit a combo of segment × market legs, each with its own direction. |
league_correlations | Realized correlation (phi) of common SGP combos across a whole league. |
prop_splits | How overs and unders settled across the whole league for one stat, split by the size of the line (stars vs role players), or by price for anytime props (HR, goals, anytime TD). |
ladder | What riding a whole alternate-line ladder would have paid: the book's main line plus each harder rung above it, staked 0.5 / 0.25 / 0.2 / 0.15 / 0.05 units by distance from the main line, graded against what happened. |
volatility | Which players swing most (or least) from game to game for a stat, after removing the swings expected from their volume. |
team_duos | How often a team's two top-lined players (by the book's line that night) both went over, or both under. |
key_numbers | Where final margins and totals land, and how often games lined at a number pushed. |
alt_lines | Estimated and historical prices for ALTERNATE game spreads and totals (full game). |
hold | The average hold (vig) each sportsbook charges by market, from closing two-way prices. |
game_flow | Rotation effects reconstructed from play-by-play. |
team_predictability | How hard a team's player props are to price, and which games tonight are most disrupted. |
minutes_overlap | How much two teammates share the floor relative to what their minutes alone predict (NBA/WNBA). 1.0 = independent, above 1 = subbed together, below 1 = staggered. |
prop_backtest | Backtest a prop bet (stat + over/under + book's closing main line) under filters such as the team's spread, the game total, home/away, the line or price, rest days, season, team/opponent, position, or the line's rank on the team (1 = the market's top option). |
describe_data | Lists the raw research tables available to query_data (box scores, play-by-play, schedules and scores, quarter/inning scores, rosters, injuries, consensus closing player-prop lines, alt-line ladders and game lines), or the columns of one table. |
query_data | Run one read-only DuckDB SELECT over the raw research tables (see describe_data). |
· If something looks wrong
- No games or props for a league: that league may be out of season, or books may not have posted props yet. Football props can appear days before kickoff, others on game day.
- A player or stat isn’t found: the tool replies with what it can match, and Claude usually retries. Try the full name, or ask which stats that league has.
- A “too many requests” error: the limit is 120 calls per 10 minutes. Wait a few minutes and try again.
- A first answer is slow: a large league file may be loading, which can take several seconds.
· Privacy and support
We log each tool call, but not your conversation or your IP address in that log. Details are in the privacy policy. Questions or problems: support@sgplab.com.