| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| pretty_name: SmartStake MLB Player Prop Odds and Results (2026) |
| tags: |
| - sports-betting |
| - sports-analytics |
| - mlb |
| - baseball |
| - odds |
| - player-props |
| size_categories: |
| - 100M<n<1B |
| task_categories: |
| - time-series-forecasting |
| - tabular-classification |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "mon=*/*.parquet" |
| --- |
| |
| # SmartStake MLB Player Prop Odds and Results (2026) |
|
|
| Minute-by-minute MLB player prop odds from ~75 sportsbooks and exchanges over the |
| 2026 season, with the graded outcome of each prop attached. Every row is one |
| book's price for one selection at one minute. This is the raw material behind |
| the study ["Sharpest Sportsbooks for MLB Player Props"](https://smartstake.app/learn/sharpest-sportsbooks-mlb-player-props). |
|
|
| ## Coverage |
|
|
| - **Odds:** late March 2026 through early July 2026. |
| - **Graded outcomes:** March through June (games that had settled at export time). |
| July rows carry odds but `result` and `won` are null. |
| - **Markets:** total bases, hits, RBIs, home runs, strikeouts, batting walks. |
| - **Books:** ~75 sportsbooks, exchanges, and prediction markets (Kalshi, ProphetX, |
| Novig, Pinnacle, DraftKings, FanDuel, Fanatics, and more). |
|
|
| ## Schema |
|
|
| | column | type | description | |
| |---|---|---| |
| | `game_id` | string | Stable per-game identity. Group and grade on this. | |
| | `start_time` | timestamp | Scheduled first pitch (UTC). Subtract `ts` for time-to-first-pitch. | |
| | `player` | string | Player name. | |
| | `market` | string | Prop market (e.g. `player total bases`). | |
| | `line` | double | The over/under number for this selection. | |
| | `side` | string | `over` or `under`. | |
| | `book` | string | Sportsbook / exchange. | |
| | `ts` | timestamp | Minute the quote was live (UTC). One row per changed minute. | |
| | `odds` | double | Decimal odds. | |
| | `result` | double | The player's actual stat for that market. Null if the game had not settled or the player was inactive. | |
| | `won` | boolean | Whether this side won. Null for a push (`result == line`) or an ungraded/void selection. | |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("SmartStake/mlb-player-props", split="train") |
| ``` |
|
|
| ```python |
| import pandas as pd |
| df = pd.read_parquet("hf://datasets/SmartStake/mlb-player-props/mon=2026-05") |
| ``` |
|
|
| ```sql |
| -- DuckDB, straight from the hub |
| SELECT book, count(*) FROM 'hf://datasets/SmartStake/mlb-player-props/**/*.parquet' GROUP BY 1; |
| ``` |
|
|
| ## Provenance, license, and responsible use |
|
|
| Odds were collected from public sportsbook and exchange feeds; outcomes are from |
| official box scores. Released under CC BY 4.0 for research and educational use. |
| This dataset is informational and historical: past prices and results do not |
| predict future outcomes, and betting carries risk of loss. Not affiliated with |
| any sportsbook. 21+. |
|
|
| ## Citation |
|
|
| > SmartStake (2026). SmartStake MLB Player Prop Odds and Results (2026). Hugging Face. |
|
|
| ## Reproduce a finding: per-book closing Brier score |
|
|
| Each book's closing-line accuracy (lower is sharper), straight from the Hub with DuckDB: |
|
|
| ```python |
| import duckdb |
| duckdb.sql("INSTALL httpfs; LOAD httpfs;") |
| print(duckdb.sql(""" |
| WITH src AS (SELECT * FROM 'hf://datasets/SmartStake/mlb-player-props/**/*.parquet'), |
| closing AS ( -- each book's last quote per selection before first pitch |
| SELECT book, market, game_id, player, line, side, |
| arg_max(odds, ts) AS odds, any_value(won) AS won |
| FROM src WHERE result IS NOT NULL AND ts < start_time |
| GROUP BY book, market, game_id, player, line, side), |
| devig AS ( -- two-way no-vig probability for the over |
| SELECT o.book, (1/o.odds)/(1/o.odds + 1/u.odds) AS p_over, o.won |
| FROM closing o JOIN closing u USING (book, market, game_id, player, line) |
| WHERE o.side='over' AND u.side='under' AND o.won IS NOT NULL) |
| SELECT book, count(*) n, round(avg((p_over - won::INT)*(p_over - won::INT)), 4) AS brier |
| FROM devig GROUP BY book HAVING n > 20000 ORDER BY brier |
| """).df()) |
| ``` |
|
|
| This pools all lines; the full study refines it to each book's main (near 50/50) line to strip the alt-line bias, and adds the crossed-market analysis. Writeup: https://smartstake.app/learn/sharpest-sportsbooks-mlb-player-props |
|
|
