From 36cd26affbf186fea63973318928fe4fb8c2ade6 Mon Sep 17 00:00:00 2001 From: lukef533 Date: Fri, 23 Jan 2026 14:39:05 -0500 Subject: [PATCH 1/5] Test presentation upload --- .DS_Store | Bin 0 -> 6148 bytes presentations/DATA400TestPrez.Rmd | 23 +++++++++++++++++++++++ 2 files changed, 23 insertions(+) create mode 100644 .DS_Store create mode 100644 presentations/DATA400TestPrez.Rmd diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..c8c32a9986fcb93e8b7c63c903dc00cc1df9b98c GIT binary patch literal 6148 zcmeHK!Ab)$5S_Hu?NSOoDD)WcTCkR?h?k|-A8)F_iF0r zK|aZP!T5@L=Sn5v!VbcVIGPR`TPG^bgE)=GIw2lL7;=3Xr$aUEsYyD_bgpj(T({~D z8qL|PbGYA92AF|gGeGBqL?!er76$dvfel?BX}m&6 zf;PP+2&F~OVqp+RP=rZEG^xTqF@#A+zqE0l#loOT2ccKSdF;x=gYXP; z%M36B%M6rFw?Xy)^!xY!auWBL0cK#W7!Z}d-|yj;Y;9fH9MxKhdW%XzafQK;6f|@x g##k!FO;j!Dmt-J%77K&uLE(#lrhyw~;7=KN2i{RorvLx| literal 0 HcmV?d00001 diff --git a/presentations/DATA400TestPrez.Rmd b/presentations/DATA400TestPrez.Rmd new file mode 100644 index 0000000..b27708b --- /dev/null +++ b/presentations/DATA400TestPrez.Rmd @@ -0,0 +1,23 @@ +--- +title: "Test Presentation Ninja" +subtitle: "⚔
with xaringan" +author: "Luke Finkielstein" +institute: "RStudio, PBC" +date: "2016/12/12 (updated: `r Sys.Date()`)" +output: + xaringan::moon_reader: + lib_dir: libs + nature: + highlightStyle: github + highlightLines: true + countIncrementalSlides: false +--- + +background-image: url(https://upload.wikimedia.org/wikipedia/commons/b/be/Sharingan_triple.svg) + +```{r setup, include=FALSE} +options(htmltools.dir.version = FALSE) +``` + +# This is a test presentation + From 60a04d4c06ffd881a03f4518911ac157a6775771 Mon Sep 17 00:00:00 2001 From: lukef533 Date: Fri, 23 Jan 2026 14:45:07 -0500 Subject: [PATCH 2/5] Create Test Presentation Ninja.html --- presentations/Test Presentation Ninja.html | 175 +++++++++++++++++++++ 1 file changed, 175 insertions(+) create mode 100644 presentations/Test Presentation Ninja.html diff --git a/presentations/Test Presentation Ninja.html b/presentations/Test Presentation Ninja.html new file mode 100644 index 0000000..2e0e178 --- /dev/null +++ b/presentations/Test Presentation Ninja.html @@ -0,0 +1,175 @@ + + + + Test Presentation Ninja + + + + + + + + + + + + + + + + + From e28e40378f09317ad85ef60939ddb38c004b6a1c Mon Sep 17 00:00:00 2001 From: lukef533 Date: Tue, 27 Jan 2026 13:45:25 -0500 Subject: [PATCH 3/5] Update .DS_Store --- .DS_Store | Bin 6148 -> 6148 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/.DS_Store b/.DS_Store index c8c32a9986fcb93e8b7c63c903dc00cc1df9b98c..41b90a1d183e98946100a995633d884b61c2b779 100644 GIT binary patch delta 14 VcmZoMXffEZk%`fC^CqSsQ2-}u1keBg delta 14 VcmZoMXffEZk%`f4^CqSsQ2-}!1knHh From ff31f61578e91fe7b8707ae9c8ddba5e64561269 Mon Sep 17 00:00:00 2001 From: lukef533 Date: Fri, 30 Jan 2026 14:44:20 -0500 Subject: [PATCH 4/5] Update .DS_Store --- .DS_Store | Bin 6148 -> 6148 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/.DS_Store b/.DS_Store index 41b90a1d183e98946100a995633d884b61c2b779..b36b49dd824c7703217a8b270d79c363c5f10963 100644 GIT binary patch delta 176 zcmZoMXfc@JFU-ckz`)4BAi%&-oRe-CoSdIquvw9LC1X8Ef|bFOA%!88ArT=76ommK z+1z{=m!zEhB%l83#J`+1hP?7q!3V1kO8qCVmuQA!{m2Na+|f7Vp%t{bNuB8 E090EjN&o-= delta 54 zcmZoMXfc@JFUrKgz`)4BAi%(o%8 Date: Fri, 30 Jan 2026 14:44:39 -0500 Subject: [PATCH 5/5] Project Idea Proposal Upload --- .DS_Store | Bin 6148 -> 6148 bytes Ideas/NBA_Game_Prediction_Proposal.md | 64 +++++++++++++++++++++ Ideas/NBA_Game_Prediction_Proposal.md.html | 48 ++++++++++++++++ 3 files changed, 112 insertions(+) create mode 100644 Ideas/NBA_Game_Prediction_Proposal.md create mode 100644 Ideas/NBA_Game_Prediction_Proposal.md.html diff --git a/.DS_Store b/.DS_Store index b36b49dd824c7703217a8b270d79c363c5f10963..a39bcc931036a007cd9dd3c9e11c5944ad73932e 100644 GIT binary patch delta 126 zcmZoMXfc=|#>B!ku~2NHo}w@Z0|Nsi1A_nqLvd1haY0f}e$wWJ%qy9BK@zMCo(w4r zsSJseS1@%jnHo$sVz!^m&(ttkmx-IFEVw8yCqFM8WWeM+X1UF)mB)qu~2NHo}w@t0|Nsi1A_nqLvc>JVQ_MOZo$UFm5hvRlkc%KPmX1i q-aL)9kZEJX8>Y?d9Q+(Wjhh8IzB5ne7jfhOY6a50% does not guarantee profit; individual game predictions are probabilistic and subject to variance. +- **Data limitations:** Historical data may not fully capture changes in league dynamics, rule changes, or roster composition over 20+ years. +- **Sample size:** Model performance is limited by the number of games available for training and testing. + +## Implications for Stakeholders + +**Sports Bettors/Fans:** Would help make informed decisions on predicting winners and increase profitability. + +**Sportsbooks:** Understand what drives betting patterns and refine odds-setting. + +**NBA Teams**: Understanding which factors affect a team's ability to win would be very interesting to coaches/players/owners. + +## Responsible Deployment & Ethics + +**Concerns:** Model could encourage problem gambling; predictions are probabilistic and not deterministic. + +**Legal:** Gambling laws vary by state (sports betting is legal in PA, both online and in person). This model would be for analysis only, not financial advice. + +**Mitigation:** Include gambling risk disclaimers, talk about it as purely academic. + +## Timeline + +- Weeks 1-2: Data collection/preparation +- Week 3: EDA and feature engineering +- Weeks 4-5: Model development and evaluation + +**Deliverable:** Trained model with accuracy metrics and feature importance analysis. \ No newline at end of file diff --git a/Ideas/NBA_Game_Prediction_Proposal.md.html b/Ideas/NBA_Game_Prediction_Proposal.md.html new file mode 100644 index 0000000..7377431 --- /dev/null +++ b/Ideas/NBA_Game_Prediction_Proposal.md.html @@ -0,0 +1,48 @@ +NBA Game Prediction Proposal.md +

Luke Finkielstein Mini Project Idea Proposal:

+

NBA Game & Stat Prediction

+

Research Question: How can we predict the outcome of NBA games with better than 60% accuracy?

+

Approach: Develop a classification model (probably logistic regression) to predict NBA game outcomes using historical game statistics and betting odds. The model will identify games with favorable edges to inform predictions.

+

Gathering Tractable Data

+

Target: Game outcomes (win/loss)

+

Key Features:

+
    +
  • Team performance metrics (record, scoring, defense)
    • +
  • Player availability/injury status
    • +
  • Opponent strength ranking
    • +
  • Game context (home/away, back-to-back games)
    • +
  • Betting odds from sportsbooks (BetGM, DraftKings, Fanduel, etc.)
    • +
+

Data sources: ESPN, NBA.com, Basektball-reference.com, Kaggle all have pre-compiled datasets of real games going back 20+ years. Additional data can be scraped from these websites if necessary. Feasibility is high—game data and odds are publicly available.

+

Retrieval & Preparation

+

Two viable approaches:

+
    +
  • Use existing public dataset (faster, reduces timeline overhead)
    • +
  • Web scrape/API calls for game stats and odds (more control, more time-intensive)
    • +
+

EDA & Insights

+

Analyze outcome variation by team strength, matchups, injuries, and game context. Identify predictive features (home-court advantage, efficiency metrics). Perform EDA and visualize feature correlations with game outcomes. Calculate correlations between candidate features and game outcomes to determine which have the strongest predictive signals. Visualizations will include scatter plots of team efficiency metrics, heatmaps of feature correlations, and distribution plots comparing home vs. away performance. I can compare the model performance against a simple baseline (like always predicting the higher-seeded team) to ensure the model adds meaningful value.

+

Potential Limitations

+
    +
  • Unpredictable events: Model cannot account for unexpected injuries, trades, coaching changes, or rest decisions made close to game time.
    • +
  • Probabilistic predictions: Accuracy >50% does not guarantee profit; individual game predictions are probabilistic and subject to variance.
    • +
  • Data limitations: Historical data may not fully capture changes in league dynamics, rule changes, or roster composition over 20+ years.
    • +
  • Sample size: Model performance is limited by the number of games available for training and testing.
    • +
+

Implications for Stakeholders

+

Sports Bettors/Fans: Would help make informed decisions on predicting winners and increase profitability.

+

Sportsbooks: Understand what drives betting patterns and refine odds-setting.

+

NBA Teams: Understanding which factors affect a team’s ability to win would be very interesting to coaches/players/owners.

+

Responsible Deployment & Ethics

+

Concerns: Model could encourage problem gambling; predictions are probabilistic and not deterministic.

+

Legal: Gambling laws vary by state (sports betting is legal in PA, both online and in person). This model would be for analysis only, not financial advice.

+

Mitigation: Include gambling risk disclaimers, talk about it as purely academic.

+

Timeline

+
    +
  • Weeks 1-2: Data collection/preparation
    • +
  • Week 3: EDA and feature engineering
    • +
  • Weeks 4-5: Model development and evaluation
    • +
+

Deliverable: Trained model with accuracy metrics and feature importance analysis.

+ + \ No newline at end of file