Last week, we shared a dataset of information from Road To Nationals for fans who want to start preparing their fantasy gymnastics drafts for the 2025 season. While this dataset provides comprehensive coverage on all returning gymnasts, some fans may also have their sights set on incoming freshmen, so we put together a separate dataset to help with that as well.
In the first tab of this spreadsheet, you will find all committed gymnasts in our recruit database for the class of 2024. We have indicated each gymnast’s highest Level 10 score on each event; event points based on our Recruit Ratings system (details here) are additionally reflected for rated gymnasts. The third tab of the spreadsheet contains some historical data to help you make sense of how the event points may translate into college performance—we have included the mean and standard deviation of scores, as well as the hit rate, of gymnasts, aggregated by recruiting class, event, and range of event points.
Finally, the second tab of the spreadsheet contains scoring information for the sixth returning gymnast on each event for each team, sorted first by NQS, then by average, then by high score. For instance, an NQS of 9.825 for a team on bars in 2024 would indicate that among returning gymnasts who are on that team’s roster for 2025, the sixth-highest NQS achieved on bars in the 2024 season was 9.825. Missing data may indicate that a team is only returning five gymnasts who recorded an NQS on an event in a given season, for example. Since assessing lineup potential can be particularly challenging for incoming freshmen, this tab may serve as a reference for how incoming freshmen may slot into their teams’ lineups. We also included data from the 2022 and 2023 season within this tab so you can compare team performances across seasons.
To use this data in your draft prep, simply select File > Make a Copy to copy the data into your own Google Sheets spreadsheet, or File > Download to export it into your spreadsheet program of choice. Happy drafting!
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Article by Dara Tan and Emma Hammerstrom
