Instead of only comparing leagues, this study looks at the team around each player. For each alt-football team-season, we measure how many teammates show up in NFL records, then check whether players from those NFL-rich teams are also more likely to appear in NFL records.
" }, { "heading": "How to read the groups", "html": "Each player gets an average teammate-density score based on the teams they played on. We sort players into 10 groups from D1 (lowest) to D10 (highest), then compare NFL reach rates across those groups.
In this snapshot, the gap between the endpoints is large: D10 sits at 24.5% while D1 is 2.6%. That spread is descriptive rather than causal, but it is too large to ignore when ranking environments.
" }, { "heading": "What the new NFL season depth adds", "html": "This version uses expanded NFL season coverage in player records, which sharpens the reached NFL after signal. Earlier snapshots often collapsed this into a coarse NFL/non-NFL flag. With season-level dating, we can now ask whether a player reached the league after a given alt-league stop rather than merely whether they ever appeared there.
That change matters most for timing: newer alt seasons are right-censored until later NFL years arrive in the data. For that reason, the league-comparison charts only include alt team-seasons that still have at least one later NFL season available for observation.
" }, { "heading": "Team-season incubators", "html": "The table ranks team-seasons by how many roster players went on to reach the NFL after that season, compared with the typical rate for the same league. That isolates teams that actually launched players into the NFL, rather than teams that simply collected NFL veterans.
The right-most columns show the share of the roster who are NFL veterans (ever in the NFL, before or after) for context. Read this jointly with the scatter and box plots: one chart captures central tendency, the others show variance and outliers.
" }, { "heading": "Variance across leagues", "html": "League averages can hide structure. Box-and-whisker views show whether a league is consistently strong or just carrying a small number of unusually productive team-seasons. In practical terms, evaluators care about both: median environment and tail upside.
In the current sample, XFL posts the highest median team-season after-rate among leagues with enough observations (median 6.0%, n=8).
" }, { "heading": "Conclusions", "html": "Data source: player files under docs/data/players/ and league-season metadata in docs/data/sports.json.
NFL season timing is taken from player.nfl.seasons when available (ESPN season rows), with NFL appearances inferred from sport_id metadata as a fallback for legacy records.
Team-season identity is inferred from roster appearances keyed by (sport_id, team). Only non-NFL North American football leagues are included in environment construction. ELF (European League of Football), EFA (European Football Alliance), and X-League (Japan) are excluded because their player pools are predominantly non-American, so near-zero NFL linkage in those leagues is structural rather than environmental. \"NFL veteran\" means the player has any NFL appearance on record (before or after the team-season). \"Reached NFL after\" means the player has at least one NFL appearance whose season is later than the team-season in question; team-seasons without a known year are excluded from the incubator ranking.
League-wide and season-cohort comparisons are right-censor adjusted: alt team-seasons are only included if at least one later NFL season exists in the source data, so recent cohorts are not penalized for lack of elapsed time.
Chart notes: scatter plots show one point per team-season; box plots summarize distributions at league level; line charts show pooled season cohorts and are weighted by roster records (not by number of teams).
This is pattern-tracking, not proof of cause and effect. Team context and NFL outcomes move together here, but that does not prove one directly causes the other.
", "table": { "columns": [ { "key": "team_season", "label": "Team-season" }, { "key": "league", "label": "League" }, { "key": "roster_size", "label": "Roster size", "numeric": true }, { "key": "nfl_after", "label": "Reached NFL after", "numeric": true }, { "key": "after_pct", "label": "Reached NFL after %", "numeric": true, "suffix": "%" }, { "key": "league_baseline_after_pct", "label": "League typical %", "numeric": true, "suffix": "%" }, { "key": "lift_pct_points", "label": "Difference (points)", "numeric": true }, { "key": "nfl_vets", "label": "NFL veterans on roster", "numeric": true }, { "key": "veteran_pct", "label": "NFL veteran %", "numeric": true, "suffix": "%" } ], "rows": [ { "team_season": "USFL 2023 STALLIONS", "league": "USFL", "roster_size": 51, "nfl_after": 7, "after_pct": 13.7, "nfl_vets": 12, "veteran_pct": 23.5, "league_baseline_after_pct": 3.8, "lift_pct_points": 9.9 }, { "team_season": "USFL 2023 PANTHERS", "league": "USFL", "roster_size": 49, "nfl_after": 5, "after_pct": 10.2, "nfl_vets": 11, "veteran_pct": 22.4, "league_baseline_after_pct": 3.8, "lift_pct_points": 6.4 }, { "team_season": "XFL 2023 BATTLEHAWKS", "league": "XFL", "roster_size": 50, "nfl_after": 5, "after_pct": 10.0, "nfl_vets": 13, "veteran_pct": 26.0, "league_baseline_after_pct": 5.7, "lift_pct_points": 4.3 }, { "team_season": "USFL 2022 BHAM", "league": "USFL", "roster_size": 69, "nfl_after": 5, "after_pct": 7.2, "nfl_vets": 13, "veteran_pct": 18.8, "league_baseline_after_pct": 3.8, "lift_pct_points": 3.5 }, { "team_season": "USFL 2022 HOU", "league": "USFL", "roster_size": 84, "nfl_after": 6, "after_pct": 7.1, "nfl_vets": 11, "veteran_pct": 13.1, "league_baseline_after_pct": 3.8, "lift_pct_points": 3.4 }, { "team_season": "XFL 2023 DEFENDERS", "league": "XFL", "roster_size": 48, "nfl_after": 4, "after_pct": 8.3, "nfl_vets": 15, "veteran_pct": 31.2, "league_baseline_after_pct": 5.7, "lift_pct_points": 2.6 }, { "team_season": "XFL 2023 RENEGADES", "league": "XFL", "roster_size": 49, "nfl_after": 4, "after_pct": 8.2, "nfl_vets": 12, "veteran_pct": 24.5, "league_baseline_after_pct": 5.7, "lift_pct_points": 2.4 }, { "team_season": "USFL 2022 PIT", "league": "USFL", "roster_size": 84, "nfl_after": 5, "after_pct": 6.0, "nfl_vets": 8, "veteran_pct": 9.5, "league_baseline_after_pct": 3.8, "lift_pct_points": 2.2 }, { "team_season": "UFL 2024 RENEGADES", "league": "UFL", "roster_size": 62, "nfl_after": 2, "after_pct": 3.2, "nfl_vets": 17, "veteran_pct": 27.4, "league_baseline_after_pct": 1.0, "lift_pct_points": 2.2 }, { "team_season": "CFL 2022 BC", "league": "CFL", "roster_size": 76, "nfl_after": 2, "after_pct": 2.6, "nfl_vets": 9, "veteran_pct": 11.8, "league_baseline_after_pct": 0.6, "lift_pct_points": 2.1 }, { "team_season": "UFL 2024 BATTLEHAWKS", "league": "UFL", "roster_size": 66, "nfl_after": 2, "after_pct": 3.0, "nfl_vets": 18, "veteran_pct": 27.3, "league_baseline_after_pct": 1.0, "lift_pct_points": 2.0 }, { "team_season": "CFL 2022 CGY", "league": "CFL", "roster_size": 84, "nfl_after": 2, "after_pct": 2.4, "nfl_vets": 7, "veteran_pct": 8.3, "league_baseline_after_pct": 0.6, "lift_pct_points": 1.8 }, { "team_season": "CFL 2022 TOR", "league": "CFL", "roster_size": 85, "nfl_after": 2, "after_pct": 2.4, "nfl_vets": 9, "veteran_pct": 10.6, "league_baseline_after_pct": 0.6, "lift_pct_points": 1.8 }, { "team_season": "CFL 2023 CGY", "league": "CFL", "roster_size": 97, "nfl_after": 2, "after_pct": 2.1, "nfl_vets": 12, "veteran_pct": 12.4, "league_baseline_after_pct": 0.6, "lift_pct_points": 1.5 }, { "team_season": "UFL 2024 STALLIONS", "league": "UFL", "roster_size": 56, "nfl_after": 1, "after_pct": 1.8, "nfl_vets": 22, "veteran_pct": 39.3, "league_baseline_after_pct": 1.0, "lift_pct_points": 0.8 } ] }, "history_row": { "player_records": 3393, "overall_nfl_veteran_pct": 9.9, "d10_pct": 24.5, "d1_pct": 2.6, "top_incubator": "USFL 2023 STALLIONS", "top_incubator_after_pct": 13.7 } }