The Injury Double Tap.The real cost of an injury in women's football.
"PITCH" is the first female-specific load index for the WSL. Built on 8,950 player-match records, a calibrated Mamdani fuzzy logic engine, and the only publicly derived female burn rate in English women's football.
No male data or proxies are used in any model. All data sources, model methodology and research insights are in the Engine Room: signalanalytics.ai/wslengineroom
ACWR from minutes played across the prior 28 days. Days between appearances from fixture data. A surface modifier based on each club's known home pitch type. Output: a readiness score out of 100 per player-match appearance.
With a club partnership we would add
Daily wellness signals (sleep, soreness, stress 1–10) via 30-second pre-training check-in. GPS load data: session RPE, sprint distance, accelerations. Prior injury type and return-to-play timeline. Medical context at the club's discretion.
Readiness bands · score out of 100
Green75 – 100
ACWR within safe range (0.8–1.3). Rest interval normal. No surface risk flag. Player available for full load. Zero injury absences in our dataset were preceded by a Green score.
Amber50 – 74
ACWR elevated (1.3–1.5) or rest interval short. Performance output statistically reduced. Rotation conversation warranted. Average pre-injury readiness in our dataset: 56.9, solidly Amber.
RedBelow 50
ACWR above 1.5 or multiple risk factors combined. Average ACWR at final appearance before a confirmed absence: 1.83. This player should not start. 100% of the 243 WSL injury gaps followed an Amber or Red flag.
02 · WHAT WAS FOUND:01.1 The Core Impact
1 in 4 appearances. Not "PITCH" ready.
Across three WSL seasons, our engine scored every player-match appearance against a female-specific readiness model. The result: a league in a near-permanent state of sub-optimal performance. No club achieved a green zone average. The women's game is asking its players to perform at the highest level while running on a readiness deficit the sport cannot currently see.
24%
of all WSL appearances between 2018 and 2021 were made by athletes our engine scores as not ready to perform optimally.
That is 2,227 player-match appearances in red. A further 6,722 in amber. Effectively zero in the green zone. The league average readiness score across all clubs, all seasons: 75.6 out of 100.
~£10M
Projected total drain · 14 clubs · 2026-27
14.4%
Of the WSL wage pool · every season
24%
Of appearances not "PITCH" ready · 3 seasons of WSL data
100%
Of injuries gave early warning signals · zero exceptions
02 · WHAT WAS FOUND:01.2 The Injury Double Tap
Card 01 · The Ghost Wage · Sunk Cost
£7.18M
Total · all 12 clubs · 2025-26
11.96%
% of £60M wage pool
The Ghost Wage
You budgeted for this player. You signed her. You are paying her. But she is in the treatment room, not on the pitch. Wages paid for zero minutes, zero goals, zero points. A 100% loss of ROI.
The calculation
WSL wage pool · published accounts~£60M
Burn rate · StatsBomb 8,950 records× 11.96%
Per club average (÷ 12)= £598k
League-wide ghost wage£7.18M
Wage pool: Arsenal £11.3M · Man United £5.88M · Liverpool £3.1M (Companies House 2024-25) · Deloitte WSL Finance Review. Burn rate: Signal Analytics from StatsBomb WSL open data · 3 seasons · 14 clubs.
Card 02 · The Drain · Unbudgeted Cost
£1.43M
Total · all 12 clubs · full season
2.4%
% of £60M wage pool
The Drain
Money nobody planned for. Every absence opens three cost lines simultaneously. "PITCH" identifies each before it happens.
Per injury · 21-day absence
Medical · MRI, consultant, physio£7,340
Points drain · 1.1 pts/100d × £18,500£4,273
Performance lag · RTP at 80%£6,000
Cost per incident£17,613
Scaled · 81 incidents/season
81 × £17,613= £1.43M
Total Drain · league-wide£1.43M
Direct measurable costs only · excludes commercial effects (sponsorship clauses, matchday revenue, jersey sales tied to player availability). The true drain is higher. Sources: GoPerform/St Lukes 2025/26 · Ekstrand et al. BJSM (WSL-adjusted 1.1 pts/100 days) · WSL commercial pool £18,500/pt · Signal Analytics 243-gap dataset.
Card 03 · The Double Tap · Total Exposure
£9.36M
Projected · 14 clubs · 2026-27
14.4%
Same rate · bigger £ problem
The Injury Double Tap
Cards 01 and 02 combined, then projected forward. The rate stays at 14.4%. The £ grows because the league gets bigger and plays more games.
Today · 12 clubs · 2025-26
Ghost Wage + Drain£8.61M · 14.4%
+ 2 new clubs joining
Ghost Wage on new clubs+£598k
Drain on new clubs+£330k
+ 4 more fixtures per club
26 vs 22 games · +18% exposure+£257k
2026-27 · 14 clubs~£9.36M · 14.4%
Cards 01 and 02 sourced above. FA WSL confirmed 14-team structure 2026-27. Howden Insurance European Football Injury Index 2025/26.
02 · WHAT WAS FOUND:01.03 Timing is the magic
The signal was always there.
100% of injury absences were preceded by an Amber or Red readiness flag. Zero were Green. The data was available. Nobody was reading it.
100%
of all 243 identified injury absences were preceded by an Amber or Red flag in our model at the final appearance before the gap.
Not a single injury absence was preceded by a Green readiness score. The predictive signal exists in real time for every club in the league. "PITCH" reads it before the injury happens.
We measured what nobody else had.
The industry uses a male 12% benchmark. Nobody had derived a female-specific rate. We built one from scratch: 3 seasons of WSL selection data, 14 clubs, 8,950 player-match records.
Female WSL burn rate: 11.96%. Almost identical to men. The assumption that women's sport is less physically demanding has been costing clubs money they didn't know they were losing.
Metric
Value
StatsBomb records analysed
8,950
WSL clubs covered
14
Probable injury gaps identified
243
Male industry benchmark
12.0%
Female WSL burn rate (derived)
11.96%
Gaps preceded by Amber / Red flag
100%
Direct medical cost · 21-day injury (GoPerform/St Lukes 2026)
£7,340
Points lost per 100 injury days (Ekstrand et al. BJSM)
0.82 pts
WSL-adjusted points lost (smaller squads)
~1.1 pts
Pre-injury signal: what the model saw
Readiness score · out of 100
Normal · league average68.1 / 100
Pre-injury · average at final appearance56.9 / 100
ACWR · acute:chronic workload ratio
Normal · safe zone midpoint1.15
Danger threshold · flag for intervention1.50
Pre-injury · average at last appearance before absence1.83
Surface risk factor
Consistent surface (same grass)+5% risk
Surface switching (grass ↔ hybrid)+30% risk
Risk in Red Zone (3× daily signals low)30%+
The Chelsea Paradox: elite clubs on premium hybrid pitches face higher injury risk on away games to natural grass. The joint adaptation works against them. It is the #1 predictor of non-contact sprains in top-division women's football.
WSL squad depth multiplier: Ekstrand et al. found 0.82 points lost per 100 injury days in men's elite football. In the WSL, smaller squad sizes (22–24 vs 28–30) push this closer to 1.1 points, meaning the financial penalty of injury is proportionally higher in the women's game.
02 · WHAT WAS FOUND:01.4 Club numbers
Select your club
Estimated wage bill
Ghost wages · first 11% · sunk cost
Total Injury Double · 22% drain
Single ACL event · fully loaded cost
2024–25 position
Recent form
Points
03 · ACTIONS:03.1 What to do NOW(proposed)
Four moves. £1M+ back on the table.
These are not guesses. Every intervention below maps directly to a validated signal in the "PITCH" engine. Each saving is derived from our 81-incident dataset at £17,613 cost per injury. Signals overlap and compound, so the £1M+ figure is deliberately conservative.
01
High Impact · Low Complexity
Daily wellness screening
Three questions. Thirty seconds. Sleep, soreness, stress scored 1–10 by each player. When all three are simultaneously low, injury risk compounds significantly across the following 72 hours. The earliest upstream signal in the model.
Based on the OSTRC-H2 framework · Clarsen, Myklebust & Bahr · Br J Sports Med, 2013 · The validated standard for athlete health surveillance in elite sport
Flag any player above ACWR 1.5 before they reach 1.83, the average pre-injury threshold we identified across 243 WSL absence gaps. Intervene with one rest day. This directly targets the pre-injury window validated at 100% accuracy.
WSL saving est.
~£565k
Complexity
Low
Time to start
2–4 wks
03
Medium Impact · Immediate
Surface switching protocol
When fixtures require moving from home surface to a significantly different away pitch, the prior week becomes an adapted training week: lower sprint volumes, more stability work. No tech required. Targets the #1 predictor of non-contact sprains.
WSL saving est.
~£105k
Complexity
Low
Time to start
Immediate
04
High Impact · Medium Complexity
Return-to-play load graduation
Players returning from injury are the highest re-injury risk group and the most expensive. A structured 3-week graduated load protocol (40% to 70% to 90% of normal ACWR) eliminates the performance lag discount.
WSL saving est.
~£335k
Complexity
Medium
Time to start
1 week
WSL saving estimates derived from Signal Analytics 81-incident dataset at £17,613 cost per injury (Card 02). Intervention prevention rates: wellness screening 30% of incidents · ACWR monitoring 40% · surface switching 6 targeted incidents · return-to-play 23% re-injury subset. Signals overlap, so the £1M+ headline applies a conservative overlap reduction. Actual outcomes will vary by club size, squad depth and implementation. Full methodology at signalanalytics.ai/wslengineroom
The bottom line
"The ghost wage is proven. The hidden bill is real. "PITCH" exists to quantify both, before the 2026 expansion makes the gap impossible to close."
03 · ACTIONS:03.2 What to do NEXT(proposed)
01
League-level briefing
This is the conversation that puts £9.36M on the table. We walk through the full findings with WSL leadership as peers, not pitching. Methodology open for scrutiny, league-wide implications discussed, partnership structures explored. One session. Full transparency.
02
Club pilot, one season
One club. One full season. "PITCH" runs live alongside existing performance staff with no disruption to current processes. At the end of the season, the club receives a financial impact report showing exactly what was saved, in pounds, per player.
03
WSL data partnership
The female-specific burn rate we derived does not exist anywhere else. We are offering WSL leadership and club medical directors first access to that asset, privately, under NDA. No public release. The data stays inside the sport and inside the room.
In partnership with signalanalytics.ai
Signal Analytics builds practical AI-led systems that reduce friction, connect signals
and help commercial teams move faster with clarity.
PITCH
Player Injury & Training Condition Health score
