As the final touchdown stats are tallied and the last fantasy points analyzed, it's time to unveil the results of Paradime's dbt™ Data Modeling Challenge - Fantasy Football Edition! Join us as we celebrate the remarkable talents of our participants and explore the groundbreaking insights they've uncovered within fantasy football data.

About Paradime's Fantasy Football Data Modeling Challenge

At Paradime, we're committed to empowering data practitioners worldwide. The Fantasy Football Edition of our dbt™ Data Modeling Challenge offered a platform for experts to showcase their skills, uncover new insights, and highlight the pivotal role of analytics engineering in cultivating data-driven organizations.

Participants were invited to transform raw fantasy football data into powerful insights using cutting-edge tools like Paradime, Snowflake, and Lightdash. The challenge not only allowed them to demonstrate their SQL, dbt™, and analytics expertise but also provided an opportunity to work with real-world fantasy football data, showcasing their creativity and analytical prowess.

Challenge Overview

Open to data analysts, analytics engineers, data engineers, data scientists, and anyone passionate about fantasy football analytics, the challenge ran from January 2 to February 4, 2025. Each participant had access to:

• Paradime for SQL and dbt™ development

• Snowflake for data warehousing and compute

• Lightdash for data analysis and visualization

• GitHub for version control and project submission

The goal was to craft SQL queries, develop dbt™ models, and create compelling visualizations that would uncover trends and tell data-driven stories from fantasy football data.

Judging Criteria

With over 300 participants and 24 standout submissions, choosing the winners wasn’t easy! Our panel of expert judges scored each project based on four key factors:

• Value of Insights – Are the insights compelling and impactful? Do they reveal something surprising or highly relevant?

• Complexity of Analysis – Does the project demonstrate strong analytics engineering expertise?

• Quality of Work – Is the SQL code, data modeling, and visualization polished and professional?

• Integration of New Data – How effectively did the participant incorporate external data (such as social media or advanced stats) into their analysis?

After thorough independent scoring and deliberation, the judges reached a consensus on the top three winners—each of whom demonstrated exceptional technical skill, analytical depth, and creativity.

Celebrating Our Top Three Participants

While these three took home the top prizes, our participants deserve recognition for tackling real-world NFL data with impressive creativity and technical expertise.

🥇 First place: Ramyashree Shetty (Data Engineer, Blazeclan Technologies)

Prize: $1,500 Amazon gift card

Submission: View Github repository

Ramya's "Value Hunters Guide" supports fantasy football strategy by combining player performance with salary data. Her analysis uncovered hidden gems using a "Moneyball" approach, complete with consistency metrics and injury analysis. Her visualizations of red zone efficiency and ROI metrics make this a must-read for any fantasy manager looking for draft day value!

Top Insights:

• Player Consistency vs. Total Points - Josh Allen leads QBs in consistency while matching top-tier fantasy production

• Red Zone Efficiency Leaders - Brandin Cooks converts 50% of red zone targets into TDs

• Highest ROI Fantasy Players - Dalvin Cook delivers 0.171 fantasy points per salary dollar, 70% more value than other top players

🥈 Second place: John Ramsey (Founder, Driftwave)

Prize: $1,000 Amazon gift card

Submission: View Github repository

John's submission stood out for its practical application and utility for fantasy managers. His Self-Service Data Mart for the 2023 NFL Season transforms raw data into an interactive analytics platform where fantasy football owners can conduct granular analysis of teams, players, games, and individual plays to inform their strategic decisions. Additionally, his comparative analysis of scoring systems across major fantasy platforms revealed how reception-heavy players (especially tight ends) and interception-prone quarterbacks are valued differently across platforms, providing key insights for managers who play in multiple leagues.

🥉 Third place: István Mózes (Data Team Lead, Munch)

Prize: $500 Amazon gift card

Submission: View Github repository

Focusing on the top 100 PPR (Points Per Reception) players, István analyzed the relationship between contract status, player availability, and fantasy performance. His comprehensive analysis revealed that players in the middle of multi-year contracts consistently outperform rookies and those on one-year deals. His findings challenge the conventional wisdom that players perform best in their "contract year," demonstrating how data-driven analysis can overturn long-held assumptions in fantasy football. For fantasy managers, this analysis offers a clear draft advantage: target durable, mid-contract players averaging 15 games per season while competitors continue chasing potentially overvalued "contract year" players.

Top Insights:

• Contract Phase Analysis - Mid-contract players outperform both rookies and players on final-year or one-year deals

• Player Availability Impact - Players averaging 15 games per season deliver the highest fantasy value

Top Insights

In no particular order, here are some of the standout insights that impressed our judges:

Red Zone Efficiency: The Hidden Key to TD Production

Author: Ramyashree Shetty (Data Engineer, Blazeclan Technologies)

Insight: Ramyashree's analysis of red zone efficiency revealed surprising patterns in touchdown conversion rates. While Brandin Cooks led all players with an impressive 50% red zone conversion rate, he had relatively few opportunities with just 6 targets. In contrast, high-volume receivers like Amon-Ra St. Brown and CeeDee Lamb received 5-6 times more red zone targets (33-35) but converted at much lower rates (21-25%). This analysis demonstrates that pure target volume doesn't necessarily translate to touchdown production, with players like Nico Collins (41.2% efficiency on 17 targets) offering an ideal balance of opportunity and conversion.

Approach: Ramya built a sophisticated data pipeline to isolate red zone plays. She first staged player-by-player data and seasonal rosters, then created a fact table specifically for red zone plays (within 20 yards of the end zone). She joined this with player dimension data and calculated efficiency metrics, revealing which players make the most of their scoring opportunities. This insight gives fantasy managers a powerful metric beyond raw touchdown totals for predicting future scoring potential.

Platform Scoring Differences: Reception vs. Interception Impact

Author: John Ramsey (Founder, Driftwave)

Insight: John's analysis revealed that reception scoring differences have far greater impact than interception penalties across fantasy platforms. He quantified reception dependency by position: tight ends (40%), wide receivers (34%), and running backs (20%). This makes tight ends disproportionately devalued in Yahoo's half-point PPR system compared to ESPN and Sleeper's full PPR, creating significant cross-platform valuation gaps for reception-dependent players.

Approach: John developed a sophisticated data model that combined play-by-play data with platform-specific scoring rules. His staging model identified key play attributes like receptions and interceptions, while his fact table compared player rankings and point totals across all three platforms (ESPN, Yahoo, Sleeper). This systematic approach identified exactly which players and positions are most affected by scoring rule variations, providing fantasy managers with crucial intelligence for platform-specific drafting strategies.

Player Availability: The Most Undervalued Fantasy Metric

Author: István Mózes (Data Team Lead, Munch)

Insight: István discovered that a player's average games played per season is a stronger predictor of fantasy value than many traditional metrics. His analysis revealed that players averaging approximately 15 games per season consistently deliver the highest PPR (Points Per Reception) fantasy output, even on a per-game basis. While players who appear in only 1-2 games annually sometimes post high per-game averages, the sweet spot for sustained fantasy production occurs at 15 games—representing players who maintain health while avoiding load management or late-season benchings. This availability-performance correlation held true across all positions analyzed.

Approach: István developed a three-stage modeling approach to uncover this insight. He first staged individual game data with complete player statistics, then created an intermediate model to aggregate season-level fantasy points and calculate per-game averages. His final fact model analyzed availability patterns across seasons, including rolling three-year averages of games played.

Snap Efficiency: Tyreek Hill's Points-Per-Play Dominance

Author: Katie Shaffer (Sr. Data Analyst, Scan.com)

Insight: Analysis of the 2023-24 NFL season revealed Tyreek Hill's extraordinary efficiency—producing 0.51 PPR points per snap played, far outpacing other elite receivers. While most top WRs logged over 1,000 offensive snaps, Hill needed just 735 to finish as WR2 overall. This finding challenges fantasy draft strategy that prioritizes players with high snap counts, showing how elite per-play efficiency can sometimes overcome volume limitations in producing top-tier fantasy results.

Approach: Katie used python to scrape player snap count data. Next , she generated a staging model for player snap counts, and finally constructed a fact table with calculated efficiency metrics including points per snap.

Red Zone Efficiency: Team-Level Touchdown Conversion Disparities

Author: Arthur Rogério (Analytics Engineer, Indicium)

Insight: Arthur's analysis revealed that red zone touchdown conversion rates vary significantly across NFL teams. The Ravens led all teams in efficiency, while the Raiders, Panthers, and Bears struggled. Most teams convert red zone trips into touchdowns at a rate between 12% and 14%, but outliers like the Ravens, Bills, Lions, and 49ers exceeded 17%, highlighting well-structured offenses.

For fantasy managers, targeting players from high-conversion teams offers a clear scoring advantage. Conversely, poor efficiency signals potential struggles in play-calling or execution—key factors when evaluating offensive players.

Approach: Arthur developed a structured pipeline to analyze red zone plays. He built a staging model for team data, a dimensional model to establish team relationships, and a fact model to calculate conversion rates and efficiency metrics.

Conclusion

A huge thank you to everyone who participated in the Fantasy Football dbt™ Data Modeling Challenge! We had an absolute blast hosting this competition, and we’re continually amazed by the creativity, technical skill, and passion from the community.

At Paradime, we take these challenges seriously. We design every competition to be fair, valuable, and rewarding—ensuring the best work gets the recognition it deserves. Our expert judges rigorously evaluate every submission to ensure the best work rises to the top. Whether you're here to sharpen your analytics skills, connect with like-minded data practitioners, or just have fun solving real-world problems, we’re here to make it worth your while.

We can’t wait to do it all again in April 2025! If you enjoyed this challenge (or are kicking yourself for missing it), make sure to pre-register and join us for the next round.