podos_soccer_model

Podos v1 Baseline

Podos is a small baseline transformer model for soccer match prediction.

Model Details

Model Description

• Developed by: Bettensor | Nickel5
• Model type: PyTorch Transformer
• Parameters 276K parameters

Uses

Podos predicts soccer match outcomes based on 23 input parameters including sportsbook odds, recent team performance, win/loss streak, and more.

Direct Use

For direct use, download the source pytorch class, label_encoder (optional), and load the model. <p><code>PodosTransformer.from_pretrained("Bettensor/podos_soccer_model")</code></p> The label encoder contains the id mappings to all teams the model was trained on. Ensure you have Torch installed with:

<p><code>pip install torch</code></p> scikit-learn version 1.4.2 if you want to use the label_encoder: <p><code>pip install scikit-learn==1.4.2</code></p> newer versions of sklearn may work but are untested.

You also need HuggingFace_hub and safetensors, install with:

<p><code>pip install huggingface_hub</code></p> <p><code>pip install safetensors</code></p>

model expects 23 parameters for input, with team names mapped as ids:

• HS - Home shots
• AS - Away shots
• HST - Home shots on target
• AST - Away shots on target
• HC - Home corners
• AC - Away corners
• HO - Home offsides
• AO - Away offsides
• HY - Home yellow card
• AY - Away yellow cards
• HR - Home red cards
• AR - Away red cards
• oddsH - Home win odds
• oddsD - Draw odds
• oddsA - Away win odds
• home_encoded - Home team id
• away_encoded - Away team id
• WinStreakHome - Home win streak
• LossStreakHome - home loss streak
• WinStreakAway - Away win streak
• LossStreakAway - Away loss streak
• HomeTeamForm - Home team recent performance
• AwayTeamForm - Away team recent performance

The label_encoder currently contains mappings for 569 unique teams

Downstream Use

Model is available to use with Bettensor at https://github.com/Bettensor/bettensor

Bias, Risks, and Limitations

podos v1 presents some home team bias, and may provide overconfident scores to its predicted outcome.

Recommendations/Future work

• reduce bias by encoding home field advantage
• more teams and leagues, especially with more rigorous performance metrics
• Additional layers for larger input size
• team embedding layers
• individual player performance

Training Data

Model was trained on 100,000 games with 569 individual teams.

• data source: https://www.football-data.co.uk/downloadm.php

Model Card Authors

qucat | Nickel5

Model Card Contact

www.nickel5.com