---
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