NBA Usage Rate, Applied to Football

Main ideas / lessons

• “Usage rate” is an adaptation from basketball to football.

  • In basketball, usage rate measures how much of the offense (the ball) a player handles while on the court.
  • In football, the goal is not “who touches the ball most,” but “who uses it most in attack”—i.e., who contributes attacking actions that can lead to the opponent regaining possession.
  • The presenter argues that basketball-style intuition doesn’t map cleanly to football touches; instead, the metric should focus on attacking “usage” events.

• “Touches” vs “usage”: the counterintuitive pattern.

  • The video claims that in the Premier League, players who touch the ball the most often use it the least (in the sense of generating fewer damaging/turnover-leading outcomes that define attacking usage in this framework).
  • A team’s pattern of touches and usage is described as something like a formation rotated on its side, helping visualize where attacking responsibility is concentrated.

• The metric is a practical approximation, but position context matters.

  • It’s described as crude for measuring attacking tendency and works better when accounting for player role/position.
  • Example concept: a striker like Hugo Ekitike may be “ball dominant for a striker” but not overall, showing how role differences affect interpretation.

• Team-level analysis is “ex post” (after the fact).

  • The approach is based on results observed in games, not predictions or projections.

• Key team concept: “gravity” / “entanglement.”

  • The video introduces a teammate-driven effect:
    • In basketball: “gravity” = performance boosted because defenders must respect threats created by teammates.
    • In American football: “entanglement” is used similarly (interconnected roles/threats).
  • Lesson: player output isn’t only individual ability—systems and teammate threats shape whether a “star” role can be sustained.

• How teams cope after losing stars depends on system structure.

  • Wolves: When Matheus Cunha and Rayan Aït Nouri were sold, Wolves allegedly lost the whole attacking system, requiring a replacement role that may not exist in the same way.
  • Brighton: Allegedly cope better because their system spreads attacking involvement so no one person monopolizes possession; therefore, losing a star doesn’t collapse the structure as much (though overall performance is still noted as weak).

• Player-level analysis: heliocentricity vs creativity gap.

  • A chart ranks the most ball-using player on each team and measures:
    • how much more they use the ball than the next-most ball dominant player (heliocentricity),
    • and how much less creative they are compared to the team’s top creator (more downward = less creative relative to the team’s best creative threat).
  • Risk highlighted: if a team’s plan depends heavily on one heliocentric player, the team becomes fragile unless that player consistently produces goals/assists.

• New “league-leap” framework for players who stay vs change.

  • Another diagram compares—within players who stayed on the same team—changes in:
    • usage rate
    • and shot-creating actions (as a proxy for creativity).
  • Regional interpretation:
    • “Big leap” (green described): more involvement and more shot creation.
    • “Just playing better”: more shot creation without increasing ball usage too much / using less while becoming more effective.
    • Some regions (yellow): require extra context.
  • Named biggest leap: Elliot Anderson (Forest).
  • Other listed “leap” players: Omari Hutchinson, Wilson Odobert, Vītālijs Jagodinskis (with caveats that Odobert/Jagodinskis’ context includes losing team stars).

• How new signings’ roles affect expectations.

  • The video argues most big signings land where they can’t immediately recreate their previous “star role.”
  • If a player is placed more left/down on the diagram, they may be:
    • trying less / behaving more unselfishly because the team doesn’t structure offense around them,
    • or failing to repeat old responsibilities that no longer work.
  • Examples:
    • Florian Wirtz can’t become Liverpool’s star if Liverpool already have one and aren’t changing structure.
    • Eberechi Eze is still adjusting because his team doesn’t play with a free number 10 role.
    • Rayan Cherki is cited as a counterexample where the team sold a similar “10,” and he is positioned differently (implying better fit/role availability).

• Striker transfers: ball dominance may correlate with success (not proven causation).

  • The presenter claims that among large striker transfers, the one who still holds the ball the most appears to perform best.
  • Generalization: top teams want different striker types (e.g., poachers staying in scoring zones), so role expectations must adjust for elite clubs.

• Man City transfer logic: Semenyo as “committee-role insurance.”

  • The video questions whether Antoine Semenyo makes sense for Man City.
  • Argument:
    • City has multiple heliocentric creators but fewer “selfish” scorers.
    • Semenyo could provide insurance when other creators aren’t at full form.
  • Fit detail:
    • At Bournemouth, Semenyo played in a shared ball committee (with Justin Kluivert, David Brooks, Marcus Tavernier), so he may accept a role that doesn’t require maximal freedom.
    • Since City may not currently provide the “maximum freedom” setup for his best output, the presenter expects no extreme GA spikes, but still sees value in specific game phases (e.g., a cup game mention).

• Editing/post-production issue

  • During editing, the presenter says advanced stats (including shot-creating actions) were wiped from FBref, prompting a search for alternative sources and requesting viewer help/attention.

Methodology / concepts presented

• Define football “usage” (basketball adaptation of usage rate).

  • Measure actions where the player’s activity could result in the opponent getting the ball, focusing on attacking contribution rather than possession duration.
  • Contrast with basketball:
    • basketball focuses on “how much ball a player has,”
    • football focuses on “how much they use the ball in attacking-risk/turnover-leading actions.”

• Avoid relying on “touches” alone.

  • Treat touches as potentially misleading because high-touch players may show lower “usage” under this definition.

• Visualization approach.

  • Compare team touches vs team usage to infer where attacking responsibility sits.
  • A version that would locate attacking actions by time and pitch location is preferred, but described as difficult.

• Use positional context when interpreting players.

  • The same metric can mean different things depending on role (e.g., striker vs overall ball usage).

• Team analysis approach.

  • Run ex post analysis:
    • describe observed behavior after results,
    • interpret how team structure changes after losing key attackers.
  • Use “gravity/entanglement” as the systemic lens for whether certain roles are replaceable.

• Player “heliocentricity vs creativity” evaluation.

  • For each team:
    • compute the gap between the top ball-dominant player and the next (heliocentricity),
    • compare that player’s creativity relative to the team’s top shot-creator (creativity gap).
  • Interpretation:
    • rightward = more heliocentric,
    • downward = relatively less creative vs the team’s best creator.
  • Conclusion: dependency on that role-holder can create sustainability issues if they don’t convert chances.

• Player “leap” framework for players who stay on the same team.

  • Compare changes in:
    • usage rate
    • shot-creating actions
  • Categorize outcomes:
    • Green region (“leap”): increases involvement and increases shot creation.
    • Other region (described as improvement): increases shot creation with less ball usage (or relatively reduced usage) → “playing better.”
    • Yellow: needs context; can be ambiguous.
  • Identify examples of biggest leap and partial progress (more involvement but not fully converting to goals/assists).

• Transfer/fit expectations using role-availability logic.

  • Big clubs often already have a star role, so newcomers:
    • cannot immediately replicate old usage patterns,
    • may have freedom/usage capped by the system.
  • Use this to temper expectations for top-side statistics.

• City-specific fit assessment.

  • Evaluate whether a signing provides:
    • a missing skill profile (more selfish scoring vs existing creator committee),
    • ability to function without needing maximum freedom.

Speakers / sources featured (as stated)

• Speaker: The channel presenter (no name given in subtitles), speaking throughout.
• Named football figures referenced (not as interviewees/speakers):
  • Hugo Ekitike
  • Matheus Cunha
  • Rayan Aït Nouri
  • João Moutinho
  • Jérémy Doku
  • Thierry (mentioned as “Thierry” / former Belgium assistant coach; name not given)
  • Noni Madueke
  • Toti Gomes
  • Lucas Paquetá
  • Yeremy Pino
  • Brennan Johnson
  • Adam Wharton
  • Ismaïla Sarr
  • Elliot Anderson
  • Omari Hutchinson
  • Wilson Odobert
  • Vītālijs Jagodinskis
  • Anton Stach
  • Granit Xhaka
  • Florian Wirtz
  • Eberechi Eze
  • Rayan Cherki (referenced again in transfer context)
  • Antoine Semenyo
  • Savio (mentioned as a City player)
  • Pedro/club players referenced for Bournemouth committee role: Justin Kluivert, David Brooks, Marcus Tavernier
  • Tottenham and other club players referenced: Son Heung-min, Kulusevski, Mohammed Kudus, Wilson Odobert, etc.
• Source website named:
  • FBref (football stats site)

Markdown Output