[HN Gopher] Show HN: I made a machine learning model to predict ...
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Show HN: I made a machine learning model to predict 66.45% of NBA
games
Introducing DeepShot: An NBA Game Prediction Model Hey devs,
sports fans, and data nerds! After weeks of work, I'm excited to
share DeepShot - an advanced NBA game predictor powered by
historical data from Basketball Reference, machine learning, and a
clean NiceGUI-powered web interface. What it does: DeepShot uses
team-level rolling averages (including Exponentially Weighted
Moving Averages) and an Elo rating system to accurately predict NBA
game outcomes. All predictions are visualized in real time through
a sleek, responsive UI. Key Features: Data-Driven Predictions
using past performance & rolling trends EWMA-based Weighted Stats
Engine Elo Ratings for contextual team strength Cross-platform
interface built with NiceGUI Key stats highlight to visualize
matchup advantages at a glance Tech Stack: Python Pandas, Scikit-
learn, XGBoost BeautifulSoup, Requests NiceGUI for the frontend
Hosted locally, runs on Windows/macOS/Linux Clone it here -
github.com/saccofrancesco/deepshot Want to see how predictive
modeling and sports analytics come together? This is for you.
Feedback, stars, forks, and PRs are more than welcome! Let me know
what you think, or drop your ideas for improvements -- always open
to suggestions! #NBA #Python #MachineLearning #SportsAnalytics
#OpenSource #NiceGUI #PredictiveModeling #GitHub #XGBoost #EWMA
#EloRating #Basketball
Author : francio445
Score : 8 points
Date : 2025-04-14 19:21 UTC (3 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| stefanfis wrote:
| I don't know how 66% compare to other prediction models like the
| ones based on classical Machine Learning algorithms. Do you have
| any comparison data?
| tianqi wrote:
| For reference, the method integrates XGBoost with SHAP models
| has an F1 score of 93.9%.
| https://pmc.ncbi.nlm.nih.gov/articles/PMC11265715/
|
| (This shows why betting on sports is almost impossible to have
| a long-term edge as it's already a very efficient market and
| the odds usually reflect the win rate very well.)
|
| As another reference, an earlier predictor that also uses the
| elo rating system has an accuracy of 65.3% which is very close
| to the result in this post, and I guess this may be a typical
| range for elo-based predictors. https://github.com/luke-
| lite/NBA-Prediction-Modeling
|
| By the way, I really like the interface of this "emailware".
| It's really fun to play with.
| skeptrune wrote:
| This is the first time I've seen the term emailware. I love the
| concept lol.
|
| Are you hosting the full application somewhere? I would love to
| try it without having to run the code myself.
| anfractuosity wrote:
| Yeah, I'd not heard of that either, I recall postcardware
| though - https://en.wikipedia.org/wiki/Shareware#Postcardware
| bonzini wrote:
| How accurate is "same result as last time the teams played"?
| bangaladore wrote:
| Excuse my lack of knowledge here.
|
| To what extent is 65% impressive? Naively, I imagine someone very
| familiar with teams and players could probably achieve similar
| results. I say this because I assume its obvious that Team A is
| better than Team B to some extent. Team A might still lose to
| Team B for whatever reason, but that's why its only 65%. And Team
| C vs Team D might be a tossup.
| RockRobotRock wrote:
| How does this compare to Vegas lines?
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