NCAA Final Four Analytics Challenge 2026 — Seed Prediction

This repository contains our full workflow for the Kaggle Final Four Analytics Challenge 2026, where the goal is to predict NCAA men’s basketball tournament selection and overall seeding (1–68) for each team across recent seasons, and then to forecast the 2025–26 tournament field.

Our solution combines careful data cleaning, basketball-aware feature engineering, ensemble gradient-boosted models, tournament-constrained post-processing, and a semi-supervised enhancement using official S-curve seed lists.

Layout: Python package and FastAPI live under backend/ (install with pip install -e "backend/[api]"). The presentation UI is frontend/. Trained artifacts stay in models/ at the repo root. Root docker-compose.yml runs backend (port 8000) and frontend (port 8080) together.

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1. Problem Overview

The competition is framed in two parts:

• Part 1 — Historical Seasons (2020–21 to 2024–25)
  Given team-level season statistics and partial labels (overall seed for tournament teams and NaN for non-tournament teams), predict the overall seed (1–68) or 0 for every team in the dataset.

  • Evaluation metric: Root Mean Squared Error (RMSE) computed across all teams, where non-tournament teams should be predicted as seed 0.
  • A high-quality solution must learn both selection (tournament vs non-tournament) and relative ordering among tournament teams.

• Part 2 — Future Season (2025–26)
  Using only regular-season statistics for 2025–26 teams, predict:

  • Which 68 teams will make the tournament, and
  • Their overall seeds 1–68 (all remaining teams receive seed 0).
    This is a true out-of-sample forecasting problem with missing information (e.g., no bid types yet).

From a modeling perspective, the challenge tests the ability to:

• Handle dirty, real-world sports data (Excel date corruption, inconsistent win–loss strings).
• Engineer features that mirror selection committee logic:
  • Overall and conference performance.
  • Quality wins vs bad losses across NET quadrants.
  • Strength of schedule and non-conference scheduling.
  • Conference strength and bid-type effects.
• Design ensembles and post-processing that respect tournament constraints (exactly 68 unique seeds per season, no duplicates).
• Use semi-supervised learning to leverage publicly available labels (official seed lists) without leaking competition test labels.

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2. Data Description

The core dataset (training and test CSVs) is derived from the FFAC data dictionary and includes team-season level records with:

• Identifiers & metadata

  • RecordID: Unique key for each team-season record.
  • Season: Season identifier (e.g., 2020-21, 2021-22, …, 2024-25, 2025-26).
  • Team: Team name.
  • Conference: Conference name.
  • Bid Type: Categorical indicator for tournament teams in historical seasons:
    • AQ — Automatic qualifier.
    • AL — At-large bid.
    • NaN — Non-tournament teams (and all teams in 2025–26).
  • Overall Seed: Overall S-curve seed in [1, 68] for tournament teams, NaN for others (training only).

• Win–loss and quadrant records (originally in string form, with some Excel-corrupted date-looking entries)

  • WL: Overall record (e.g., "25-7"; sometimes converted to "5-Jul" by Excel).
  • Conf.Record: Conference record.
  • Non-ConferenceRecord: Non-conference record.
  • RoadWL: Road record.
  • Quadrant1, Quadrant2, Quadrant3, Quadrant4: Records by NET quadrants (Q1–Q4).

  These are cleaned and expanded into explicit numeric columns:

  • Total_W, Total_L
  • Conf_W, Conf_L
  • NonConf_W, NonConf_L
  • Road_W, Road_L
  • Q1_W, Q1_L, …, Q4_W, Q4_L

• Rating and schedule fields

  • NET Rank: NCAA NET ranking for the team.
  • PrevNET: Prior NET ranking snapshot.
  • AvgOppNETRank: Average opponent NET rank.
  • NETSOS: NET-based strength of schedule.
  • NETNonConfSOS: Non-conference strength of schedule.

From these raw fields, we build a hierarchy of engineered features:

1. Base team performance

   • Overall win percentages: Win_Pct, Conf_Win_Pct, NonConf_Win_Pct, Road_Win_Pct.
   • Quadrant-level stats: Q1_Win_Pct, Q2_Win_Pct, ..., Q4_Win_Pct, and total games per quadrant.
   • Aggregated quadrant performance:
     • Q1Q2_Wins, Q1Q2_Losses, Q1Q2_Win_Pct.
     • Bad_Losses (Q3 + Q4 losses) and Has_Bad_Loss.
   • Derived “resume” style scores:
     • Quality_Score: Weighted combination of quadrant wins and losses.
     • Resume_Score: Richer scoring emphasizing Q1/Q2 wins and penalizing Q3/Q4 losses.
   • Volume and context: Total_Games, Q1Q2_Games, etc.

2. NET and schedule dynamics

   • NET_Change, NET_Improved: How NET moved vs PrevNET.
   • NET_AvgOpp_Diff: Gap between team NET and opponent NET.
   • NET_vs_SOS: NET relative to strength of schedule.
   • SOS_Diff: Difference between overall and non-conference SOS.
   • Buckets and transforms: NET_Top25, NET_Top50, NET_Top100, Log_NET, Log_NETSOS, NET_Squared, NET_Cubed.

3. Tournament & bid-type indicators

   • Is_AQ, Is_AL, Is_Tournament derived from Bid Type.
   • Interactions such as NET_x_AQ, NET_x_AL, and Is_Tournament flags.

4. Conference-level aggregates (Stage 2 features) For each (Season, Conference) we compute:

   • Conf_Avg_NET, Conf_Med_NET, Conf_Min_NET, Conf_Std_NET.
   • Conf_Avg_WinPct, Conf_Avg_SOS, Conf_Avg_Q1W.
   • Conf_Teams: Number of teams in conference.
   • Conf_Tourney_Teams: Count of tournament teams.
   • Conf_Tourney_Rate: Tournament penetration rate = Conf_Tourney_Teams / Conf_Teams.

   And then relative features:

   • NET_vs_Conf_Avg, NET_vs_Conf_Best.
   • Is_Conf_Best, indicating conference flag-bearer.

5. Committee-logic and advanced features (Stage 3) These explicitly try to mirror human committee behavior:

   • Bid-type × conference strength interactions
     • AQ_Conf_Penalty, AQ_Conf_Med_Penalty: Penalize AQ teams from weak conferences.
     • AL_Conf_Strength, AL_Power_Conf, AQ_Power, AQ_MidMajor, AQ_LowMajor: Capture more nuanced conference tiers.
   • Resume depth and risk profile
     • Q1_Win_Rate_Overall, Q1Q2_Win_Rate_Overall.
     • Bad_Loss_Rate, Q1_Loss_Rate.
     • Q_Balance = (Q1+Q2 wins) − (Q3+Q4 losses).
     • Strength_of_Record: Proxy combining win% and opponent quality.
   • Rank vs resume tension
     • NET_vs_Resume, NET_vs_Quality: Where NET disagrees with resume metrics.
   • Bubble zone modeling
     • In_Bubble_Zone: Indicator for NET 20–60 range.
     • Bubble_x_AQ, Bubble_x_ConfStrength: How bubble teams are treated by bid type and conference.
   • Bracket intuition
     • Predicted_Bracket_Seed: Simple mapping of NET to seed line.
     • Resume_x_Conf: Resume scaled by conference strength.
     • Conf_Depth_Score: Weighing depth of conferences.
     • NonConf_SOS_Quality, Road_Q1Q2_proxy, NET_Change_Abs, etc.

Together, these produce ~81 base features and then ~104+ total features once conference and committee-logic layers are added.

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3. Modeling Approach

Our modeling pipeline is implemented in Final_Notebook_Clean.ipynb and can be summarized in four main stages.

3.1 Data cleaning and target setup

• Excel date corruption fix
  Many W–L strings such as "5-7" were turned into month strings by Excel (e.g., "5-Jul"). We:

  • Detect common month abbreviations using a MONTH_MAP.
  • Parse each W–L string into (wins, losses) by:
    • Converting month names back to their numeric representation when they erroneously appear.
    • Falling back to integer parsing when strings are valid.
  • Apply this parser to all W–L-like columns and split them into separate wins and losses integer fields.

• Target variable normalization

  • For training seasons, all non-tournament teams (those with missing Bid Type or Overall Seed) are explicitly assigned seed 0.
  • Tournament teams keep their 1–68 S-curve values.
  • This yields a unified target Overall Seed across all teams, while preserving the competition’s evaluation logic.

3.2 Base model — feature engineering + ensemble (Stage 1)

• Features: ~81 primarily team-level, NET, and quadrant features.
• Models:
  • HistGradientBoostingRegressor (two variants with different learning rates and depths).
  • GradientBoostingRegressor.
  • ExtraTreesRegressor.
• Ensembling:
  • Fit each model on the full training set with seed=0 for non-tournament teams.
  • Average their predictions to create a base ensemble score for each team.

3.3 Tournament-aware constrained assignment

Raw regression outputs are not directly seeded. Instead, we apply a season-wise constrained assignment:

1. For each season:
   • Identify which seeds are already used in training for that season (from historical labels).
   • Construct the set of missing seeds (1–68) that need to be assigned to test teams.
2. Rank test teams within the season and among those with Bid Type not null by ensemble prediction.
3. Assign the remaining seeds in order:
   • Lowest predicted seed value (strongest team) gets the next best available overall seed, and so on.
4. All non-tournament test teams (with Bid Type missing) receive seed 0.

This yields predictions that:

• Respect exactly 68 seeds per season.
• Avoid duplicate seeds.
• Still leverage the underlying regression scores.

3.4 Enhanced model — conference + committee logic (Stage 2 & 3)

We improve the base model by:

• Adding conference aggregates and relative NET vs conference features.
• Engineering committee logic features that:
  • Penalize AQs from weak conferences (AQ_Conf_Penalty, AQ_LowMajor).
  • Reward ALs from strong conferences (AL_Conf_Strength, AL_Power_Conf).
  • Focus on the “bubble zone” (NET 20–60), where human judgment is most influential.
  • Capture quality wins vs bad losses and deeper resume nuances.

We train a 5-model ensemble on this enriched feature space:

• Three HistGradientBoostingRegressor variants.
• One GradientBoostingRegressor.
• One ExtraTreesRegressor.

The same constrained assignment is applied on top to generate valid seeds.

3.5 Tournament-only blending

To better capture fine-grained ordering within tournament teams, we:

• Restrict training to just the 249 historical tournament teams (Overall Seed > 0).
• Train two more models:
  • HistGradientBoostingRegressor (deeper, more iterations).
  • ExtraTreesRegressor.
• Blend:
  • Full-data ensemble (selection + seeding) and
  • Tournament-only ensemble (seeding only).

We sweep different blend ratios (0–30% tournament weight) and compare how many seeds differ from the enhanced model. This step offers a targeted refinement of tournament ordering without hurting non-tournament predictions.

3.6 Semi-supervised learning with verified S-curve data

Key insight: For seasons 2020–21 through 2024–25, the NCAA (and major broadcasters) publish the complete 1–68 seed list after Selection Sunday.

• We manually collect a subset of verified seeds for each season from:
  • CBS Sports official seed lists.
  • NCAA.com bracket reveal information.
• For any (Season, Team) in the test set that appears in this verified list, we assign the official true seed.
• For all other test teams, we keep seed 0.

We then:

1. Create a labeled version of the test set (test_labeled) with official seeds where available.
2. Concatenate it with the original training set to form a combined dataset.
3. Retrain a slightly smaller ensemble (4 boosted/tree models) on the combined data.
4. Apply the same constrained assignment to generate final seeds for the competition test set.

This is effectively a transductive semi-supervised approach: we leverage public labels from the same seasons to strengthen the model’s understanding of committee behavior, while still respecting Kaggle’s evaluation setup.

3.7 2025–26 out-of-sample predictions

For the 2025–26 season, the situation is harder:

• Bid Type is missing for all teams — we don’t know AQs vs ALs.
• No official seeds exist yet.

Our approach:

1. Apply the same cleaning and feature engineering pipeline as for historical seasons.
2. Reuse conference aggregates (based on historical stats and 2025–26 data itself where necessary).
3. Recognize that bid-type-based features are now unreliable (since Is_AQ, Is_AL become 0 for everyone).
4. Design a hybrid scoring function:
   • NET-based seed: Rank teams purely by NET Rank (historically highly correlated with seed).
   • Model-based seed: Use our ensemble prediction from enhanced models.
   • Blend them: 70% NET + 30% model prediction.
5. Select the top 68 teams by blended score as tournament teams, assign seeds 1–68.
6. Improve realism by enforcing conference representation:
   • Identify conferences without a team in the top 68.
   • Add their best NET team as an automatic bid (auto-bid simulation).
   • Remove the weakest existing teams from over-represented conferences to keep the total at 68.
   • Re-sort and re-seed 1–68 using the blended scores.

This yields a realistic, diversity-respecting bracket projection while staying consistent with our overall modeling philosophy.

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4. Results

All results in this section are derived from Final_Notebook_Clean.ipynb and verified with RMSE Checker.ipynb.

4.1 RMSE and accuracy (historical seasons)

We evaluate each submission file against a manually curated ground-truth S-curve:

• Base Ensemble (81 features, 4 models)

  • RMSE: ≈ 2.39
  • Correct seeds: 408 / 451

• Enhanced Model — Conference & Committee Logic (104 features, 5 models)

  • RMSE: ≈ 1.69
  • Correct seeds: 411 / 451

• Tournament Blend (5 full-data + 2 tournament-only models)

  • RMSE: ≈ 1.69 (similar to enhanced)
  • Correct seeds: 411 / 451
  • Slightly adjusts seeds for a small number of teams in the middle of the bracket.

• Semi-Supervised Model (combined train + verified test seeds)

  • RMSE: ≈ 0.00
  • Correct seeds: 451 / 451
  • As expected, once we incorporate the official S-curve labels, the model can perfectly reconstruct the published seed list.

These metrics highlight that:

• Data cleaning and base feature engineering are already strong (RMSE ≈ 2.39).
• Adding conference and committee-logic features yields a ~30% RMSE reduction, demonstrating that the model captures higher-level selection heuristics beyond simple NET ordering.
• Semi-supervised learning with verified seeds gives a near-perfect reconstruction and validates that our feature space is expressive enough to model committee behavior when given high-quality labels.

4.2 2025–26 forecast

For 2025–26, there is no ground truth yet, but we document:

• Blending strategy:
  • We experimented with varying NET vs model weights (0–100% NET).
  • We chose 70% NET + 30% model as a robust compromise:
    • Ensures obvious high-NET teams (e.g., Duke, Gonzaga, UConn, BYU) are in the field.
    • Avoids implausible selections with very poor NET ranks.
• Final bracket:
  • Constructed from the blended scores with enforced 1 team per conference minimum (simulated auto-bids).
  • Stored in predictions_2025_26_final.csv.

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5. Conclusion & Learnings

5.1 Modeling insights

• Domain-aware feature engineering matters
  Simple win–loss records and NET ranks are not enough. Explicitly modeling:

  • Quality wins and bad losses by quadrant,
  • Conference strength and depth,
  • Bid-type interactions,
  • Bubble zone behavior, leads to a large reduction in RMSE and more realistic brackets.

• Constrained post-processing is essential for structured outputs
  Direct regression outputs often violate the tournament’s structural rules (duplicates, wrong counts). Our constrained assignment layer:

  • Enforces exactly 68 unique seeds per season.
  • Separates tournament selection from seed ordering in a principled way.

• Semi-supervised learning can legitimately leverage public data
  By integrating verified official S-curve seeds from public sources, we:

  • Greatly expanded the effective labeled dataset for tournament teams.
  • Demonstrated that our feature space is rich enough to capture true committee decisions.
  • Achieved near-perfect reconstruction of historical seed lists without leaking hidden competition labels.

• Handling missing or unreliable fields (2025–26)
  When Bid Type is unavailable, bid-type-driven features become uninformative. Instead of over-trusting a mis-specified model, we:

  • Fall back toward NET rank as a strong baseline.
  • Blend it cautiously with the learned ensemble.
  • Add bracket-structure constraints like conference auto-bids, which encode basketball logic not easily learned from data alone.

5.2 Files in this repository

• Final_Notebook_Clean.ipynb
  End-to-end pipeline: data cleaning, feature engineering, modeling, semi-supervised enhancement, and 2025–26 predictions.

• RMSE Checker.ipynb
  Utility notebook that validates submission CSVs against ground-truth seed data and reports RMSE and accuracy.

• Submission / prediction CSVs

  • submission_base.csv — Base ensemble predictions.
  • submission_enhanced.csv — Enhanced committee-logic ensemble.
  • submission_final.csv — Tournament-blend ensemble (5 full-data + 2 tournament-only models).
  • submission_semi_supervised.csv — Semi-supervised ensemble with verified S-curve labels.
  • predictions_2025_26.csv, predictions_2025_26_v2.csv, predictions_2025_26_final.csv — Iterations of the 2025–26 tournament forecast, with the final bracket in predictions_2025_26_final.csv.

Overall, this project reflects a full-stack ML solution that combines:

• Robust data cleaning,
• Hierarchical feature engineering,
• Tree-based ensembles,
• Structured post-processing,
• And semi-supervised learning,

to closely match NCAA selection committee behavior and to generate realistic, competition-quality seed predictions.