Not all fantasy football leagues are equal. Scoring system differences across ESPN, Yahoo, and Sleeper impact player rankings and strategy. This post breaks down key scoring variations and analyzes how they affect player performance. I explored these differences in the dbt Fantasy Football Data Modeling Challenge, hosted by Paradime.io and Lightdash. This submission placed second in the competition. Understanding these nuances can give fantasy managers an edge when selecting players or transitioning between platforms.
The Game Plan
- Get scoring data from ESPN, Yahoo, and Sleeper into our warehouse.
- Analyze how each platform handles fantasy points (and which players get the short end of the stick).
- Summarize everything in a Lightdash dashboard to make it digestible.
Exploring DinoAI in dbt Development
With the game data provided in the challenge, it was relatively easy to join this with the existing scoring methodologies. One feature I wanted to experiment with during this challenge was Paradime’s DinoAI. It offers autocompletion, in-line suggestions, and a chat window built directly into the IDE—similar to GitHub Copilot or Amazon Q.
After writing a query to calculate fantasy football points based on game events, I tested DinoAI by instructing it to generate the stg_fantasy_ppg_by_site model in staging.yml. While it required some adjustments, DinoAI successfully generated the model for insertion into the YAML file.
What Worked Well with DinoAI
- Autocomplete: Having used GitHub Copilot’s autocomplete for years, this feature now feels essential in any IDE. DinoAI handled it well.
- Automatic Commit Messages: As someone who doesn’t always write detailed commit messages, I found this feature invaluable. Instead of vague messages like “fixed model A” (something I actually wrote last week), DinoAI automatically generated clear, structured commit messages.
- Chat Panel: I mostly used this feature to review past conversations, but it was still a helpful reference during development.
Areas for Improvement
- In-line Chat: While functional, the in-line chat popup appeared a second or two after clicking a line, which became slightly distracting.
- Generate Tests / Find Issues and Fix: Similar to my experience with GitHub Copilot, the auto-generated tests and issue detection didn’t always align with what I was looking for. The suggestions weren’t necessarily wrong, but they weren’t particularly useful either.
Stock Scoring System Differences
With the data in place, I was able to start digging into how each site calculates fantasy scoring.
As of the 2023 season, the most notable scoring differences among ESPN, Yahoo, and Sleeper were:
- Points per reception (PPR): Yahoo awards 0.5 PPR, while ESPN and Sleeper use full PPR (1 point per reception).
- Interceptions thrown: ESPN penalizes quarterbacks -2 points per interception, whereas Yahoo and Sleeper use -1 point.
- Other categories such as touchdowns, yards, and fumbles remain consistent across all three platforms.
These differences may seem minor, but they significantly impact how players are valued in different leagues.
How Scoring Differences Affect Players
Total Point Impact: Receptions vs. Interceptions
In 2023, the total points gained from receptions far outweighed the points lost from interceptions. This had several implications:
- Quarterbacks: The first QB whose rankings differed across platforms was Sam Howell—14th in ESPN but 13th in Sleeper and Yahoo. The smaller interception penalty on Yahoo and Sleeper benefited QBs with higher turnover rates.
- Reception-heavy players: The Yahoo scoring system penalized players who rely on short receptions, as they received fewer points per catch.
- Positional impact:
- Running Backs (RBs): Since only ~20% of their points come from receptions, they were least affected.
- Wide Receivers (WRs): With ~34% of points from receptions, they saw a moderate impact.
- Tight Ends (TEs): Since ~40% of their points come from receptions, they were most impacted by Yahoo’s scoring system.
Players who thrive on short, high-volume receptions (e.g., slot receivers and pass-catching TEs) saw the most dramatic ranking shifts between platforms. Further analysis could explore whether these players are designed short-yardage targets or primarily check-down options.
The Most Impacted Players & Theories
1. Tyler Conklin
- Yahoo Outlook: A mid TE2 in ESPN leagues, Conklin scored 24.8% fewer points in Yahoo, dropping below Hunter Henry and Isaiah Likely.
- Takeaway: Players on high-pressure teams (e.g., the Jets, with a 45% pressure rate) may struggle in Yahoo leagues if their receptions primarily come from check-downs.
2. Evan Engram
- Yahoo Outlook: While Engram ranks behind only Kelce and LaPorta in ESPN, he falls in Yahoo rankings due to short-yardage receptions.
- Takeaway: Unlike many TEs, Engram’s high reception count comes from designed screens rather than check-downs, making him an interesting case study.
3. Chigoziem Okonkwo
- Yahoo Outlook: Low red-zone involvement and only one touchdown from Derrick Henry limited Okonkwo’s impact in Yahoo leagues.
- Takeaway: Yahoo’s scoring makes red-zone efficiency more critical for TE fantasy success.
4. Dalton Kincaid
- Yahoo Outlook: 23% fewer points in Yahoo caused Kincaid to fall just below Dalton Schultz (HOU) in Yahoo rankings.
- Takeaway: Despite Yahoo’s half-PPR penalty, Kincaid still remained a low-end TE1 across all platforms.
Why This Matters for Fantasy Managers & Analytics Teams
These scoring differences across platforms have significant implications for both fantasy football strategy and data analytics. These tiny rule changes completely shift player value, and if you’re not adjusting your strategy across platforms, you’re probably leaving points (and wins) on the table.
From a data modeling perspective, this analysis showcases the power of dbt and Lightdash in building a self-serve analytics platform that uncovers hidden insights in fantasy data. By structuring data effectively, defining granularity, and applying real-world testing, we can ensure that analytics tools provide actionable insights—whether in fantasy sports or business intelligence.
At Driftwave, we specialize in self-service analytics hosting for tools like Lightdash, helping teams unlock the full potential of their data. If you’re interested in building better analytics for your organization, we offer three months of free Lightdash hosting for teams already using dbt.