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soccer_stats

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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x96 in position 295: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 233, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2998, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1918, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2093, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__
                  for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 188, in _generate_tables
                  csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.pd_read_csv_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 75, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1212, in xpandas_read_csv
                  return pd.read_csv(xopen(filepath_or_buffer, "rb", download_config=download_config), **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
                  return _read(filepath_or_buffer, kwds)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 620, in _read
                  parser = TextFileReader(filepath_or_buffer, **kwds)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
                  self._engine = self._make_engine(f, self.engine)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1898, in _make_engine
                  return mapping[engine](f, **self.options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 93, in __init__
                  self._reader = parsers.TextReader(src, **kwds)
                File "parsers.pyx", line 574, in pandas._libs.parsers.TextReader.__cinit__
                File "parsers.pyx", line 663, in pandas._libs.parsers.TextReader._get_header
                File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "parsers.pyx", line 2053, in pandas._libs.parsers.raise_parser_error
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x96 in position 295: invalid start byte

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Dataset Card: Soccer Stats Database

Dataset Summary

The Soccer Stats Database is a structured dataset built for analyzing and optimizing profits in football betting. The dataset includes historic and upcoming match results, team statistics, betting odds, model inference results, and optimization outcomes. It is designed to provide comprehensive data for exploring and implementing models for sports betting optimization, as discussed in the accompanying article on my blog.

This dataset was finalized on December 5, 2024, and offers information across multiple tables, enabling researchers, data scientists, and enthusiasts to experiment with predictive modeling, decision-making under uncertainty, and utility-based optimization in sports betting.

Dataset Structure

The dataset consists of five main tables:

  1. fbref_results

    • Description: Contains match information from the FBref website. Includes both historic and upcoming matches.
    • Columns:
      • League, date, and time of the match.
      • Home and away teams.
      • Scores and results (if available).

  2. sofifa_teams_stats

    • Description: Provides team statistics at specific update times, reflecting their overall performance.
    • Columns:
      • Team name, date of update.
      • Metrics such as overall score, attack, build-up speed, and more.

  3. soccer_odds

    • Description: Covers odds data from bookmakers for different match outcomes, with updates over time.
    • Columns:
      • Match ID, bookmaker, outcome type, odds, and update time.
      • Match commence time, league, home and away teams.

  4. models_results

    • Description: Contains results of model inference for matches.
    • Columns:
      • Match ID, inference date-time, model used.
      • Predicted probabilities for outcomes, match details.

  5. optim_results

    • Description: Includes optimization results based on model inferences, utility functions, and bookmaker odds.
    • Columns:
      • Match ID, optimization date-time, model used, best odds, and corresponding bookmakers.
      • Bankroll allocation fractions, inferred probabilities, and utility function details.

Applications

  • Predictive Modeling: Train models to predict football match outcomes.
  • Betting Odds Analysis: Compare and analyze odds across bookmakers.
  • Utility-Based Optimization: Allocate bankroll using inferred probabilities and a utility function to maximize long-term gains.
  • Research & Development: Develop and evaluate innovative methods in sports analytics and decision-making.

Usage Notes

  • The dataset is ideal for machine learning, time-series analysis, and statistical modeling.
  • Users are encouraged to read the detailed explanation of the project on my blog for insights into the methodology and use cases.
  • The data should be processed and used in compliance with ethical and legal standards, especially when scraping and utilizing bookmaker odds.

Citation

If you use this dataset in your work, please cite it as follows:
Julien Delavande, "Soccer Stats Database: Optimizing Gains in Football Betting," 2024. Blog.

Contact

For questions or collaborations, reach out via my blog or email.

Enjoy exploring and optimizing!

license: apache-2.0

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