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sportsanalysis

Aggregate NBA sports prediction software with machine learning (e.g NHiTS, TFT, LSTM, GNN).

https://github.com/bonelesswater/sportsanalysis

Science Score: 44.0%

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    CITATION.cff file
    Found CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (10.6%) to scientific vocabulary
Last synced: 9 months ago · JSON representation ·

Repository

Aggregate NBA sports prediction software with machine learning (e.g NHiTS, TFT, LSTM, GNN).

Basic Info
  • Host: GitHub
  • Owner: BonelessWater
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 298 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 1
  • Open Issues: 1
  • Releases: 0
Created over 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme Citation

README.md

The System

System structure - 1. data pipeline - information from the book; trades - quantitative game and player data relavent to sport association - qualitative news sentiment data - 2. runs models Models folder |_ examplenewssentamentmodel.py | examplearbitragemodel.py - 3. finds buy-sell signals - normalized data to translate into execution - 4. executes trades - logs into broker w/ selenium - 5. PnL data - shows results with graphs and helpful multiples

the strategy might be composed of two general trading systems: - arbitrage; making the market more efficient - news sentiment; based on highly relavent and ripe data

news sentiment - classification method

brainstorming new sentiment: - what are the possible new items that have high importance - is there an objective way of determining that

- should players have a preformance rating to determine what news is important and how

brainstorming ai: - how can we use ai to find buy-sell signals - is there a way of formatting data in a clean way that the neural network can understand - is there enough data to train an ai model - how long would it take to run and would it be competetive - can we use a general/trained LLM model or might it be better to only use quantitative terms - can both be used but for different scenarios; news sentiment & regular buy-sell model

Weird set-up

To fix the MRO error, you need to patch the pytorch‑forecasting source code that defines the combined callback. In your environment, locate the file:

vbnet Copy venv\lib\site-packages\pytorchforecasting\models\temporalfusion_transformer\tuning.py Then find the class definition that currently looks like this:

python Copy class PyTorchLightningPruningCallbackAdjusted(pl.Callback, PyTorchLightningPruningCallback): ... Change the inheritance order so that the Optuna integration callback comes first:

python Copy class PyTorchLightningPruningCallbackAdjusted(PyTorchLightningPruningCallback, pl.Callback): ... Save the file and re-run your script. This swap resolves the inconsistent method resolution order error by ensuring that the MRO is defined in a consistent way.

Owner

  • Name: Dom
  • Login: BonelessWater
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Dominick Dupuy Desangles"
title: "SportsAnalysis"
version: 1.0.0
doi: 10.5281/zenodo.1234
date-released: 2025-03-12
url: "https://github.com/BonelessWater/SportsAnalysis"

GitHub Events

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  • Push event: 8
  • Public event: 1
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Last Year
  • Push event: 8
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  • Pull request event: 1
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  • BonelessWater (1)
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