goto-conversion 4.0.2

Powered $47,000 of prize money, 10+ Gold Medals and 100+ Medals on Kaggle

goto_conversion - Powered $47,000 of prize money, 10+ Gold Medals and 100+ Medals on Kaggle

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Wins

goto_conversion has powered $47,000 of prize money, 10+ :1st_place_medal: gold-medal-winning solutions and 100+ :2nd_place_medal: :3rd_place_medal: medal-winning solutions on Kaggle [1]. They include:

• 1x :1st_place_medal: 2x :2nd_place_medal: 2x :3rd_place_medal: 5x Medals including 1x Gold (all solo) the Founder of goto_conversion won from 2019 to 2025 Basketball Outcome Prediction Competitions :basketball:
• 70x :2nd_place_medal: 70xSilver Medal (14th to 83rd place out of 1727) Solution from 2025 Basketball Outcome Prediction Competition :basketball:
• 15x :3rd_place_medal: 15xBronze Medal (86th to 100th place out of 821) Solution from 2024 Basketball Outcome Prediction Competition :basketball:
• 15x :3rd_place_medal: 15xBronze Medal (208th to 222nd place out of 3462) Solution from 2026 Basketball Outcome Prediction Competition :basketball:
• :moneybag: $8,000 Winner :1st_place_medal: Gold Medal (2nd out of 1727) Solution from 2025 Basketball Outcome Prediction Competition :basketball:
• :moneybag: $7,000 Winner :1st_place_medal: Gold Medal (3rd out of 1727) Solution from 2025 Basketball Outcome Prediction Competition :basketball:
• :moneybag: $5,000 Winner :1st_place_medal: Gold Medal (5th out of 1727) Solution from 2025 Basketball Outcome Prediction Competition (referred to as kaito510 solution) :basketball:
• :moneybag: $5,000 Winner :1st_place_medal: Gold Medal (6th out of 1727) Solution from 2025 Basketball Outcome Prediction Competition :basketball:
• :moneybag: $5,000 Winner :1st_place_medal: Gold Medal (7th out of 1727) Solution from 2025 Basketball Outcome Prediction Competition :basketball:
• :moneybag: $5,000 Winner :1st_place_medal: Gold Medal (8th out of 1727) Solution from 2025 Basketball Outcome Prediction Competition :basketball:
• :moneybag: $7,000 Winner :1st_place_medal: Gold Medal (3rd out of 821) Solution from 2024 Basketball Outcome Prediction Competition :basketball:
• :moneybag: $5,000 Winner :1st_place_medal: Gold Medal (4th out of 821) Solution from 2024 Basketball Outcome Prediction Competition :basketball:
• :1st_place_medal: Gold Medal (14th out of 3225) Solution from 2023 Stock Market Prediction Competition (the zero_sum variant) :chart_with_upwards_trend:
• 3x :1st_place_medal: 3xGold Medal (10th to 12th out of 1727) Solutions from 2025 Basketball Outcome Prediction Competition :basketball:
• Top 1% :2nd_place_medal: Silver Medal (33rd out of 3462) Solution from 2026 Basketball Outcome Prediction Competition :basketball:
• Top 3% :2nd_place_medal: Silver Medal (97th out of 3462) Solution from 2026 Basketball Outcome Prediction Competition :basketball:
• Top 5% :2nd_place_medal: Silver Medal (38th out of 821) Solution from 2024 Basketball Outcome Prediction Competition :basketball:
• :white_check_mark: Approved by PySport :trophy:

Ease of Use

To use goto_conversion, it does not require historical data for model fit, advanced domain knowledge, nor paid computational resources. Linked below provides five examples of how to use goto_conversion in the freely available, Google Colab.

Abstract

Our proposed method goto_conversion reduces all inverse odds by the same units of standard error. This attempts to consider the favourite-longshot bias by utilising the proportionately wider standard errors implied for inverses of longshot odds and vice versa.

This repository's main purpose is to implement goto_conversion, but also implements some other functions, such as efficient_shin_conversion. The Shin conversion [2] is originally a numerical solution, but according to [3], we can enhance its efficiency by reducing it to an analytical solution. We have implemented the enhanced Shin conversion as efficient_shin_conversion in this package.

The favourite-longshot bias is not limited to betting markets; it exists in stock markets too. Thus, we applied the original goto_conversion to stock markets by defining the zero_sum variant. Under the same philosophy as the original goto_conversion, zero_sum adjusts all predicted stock prices (e.g. weighted average price) by the same units of standard error to ensure all predicted stock prices relative to the index price (e.g. weighted average NASDAQ price) sum to zero. This attempts to consider the favourite-longshot bias by utilising the wider standard errors implied for predicted stock prices with low trade volume and vice versa.

References

[1] goto_conversion's Kaggle Profile

[2] E. Štrumbelj, "On determining probability forecasts from gambling odds". International Journal of Forecasting, 2014, Volume 30, Issue 4, pp. 934-943.

[3] Kizildemir, Melis, Akin, Ertugrul and Alkan, Altug. "A family of solutions related to Shin’s model for probability forecasts" Journal of Quantitative Analysis in Sports, vol. 21, no. 2, 2025, pp. 153-158.

Contact Me

via LinkedIn: https://www.linkedin.com/in/goto/