Psychology Resources
Methodology · FPT5 min read

How FPT Works

Players complete a sport-specific questionnaire and receive a personalised personality profile. Coaches see every player's profile in a shared dashboard, alongside squad-level analysis and coaching guidance. Here is exactly how the system works — and what research underpins each component.

Last updated: May 2026

FPT is free to try. Coaches can set up a squad and invite players to complete the questionnaire with no commitment required.

Try FPT with your squad →

The Process at a Glance

📝Step 01

Player completes questionnaire

Sport-specific questions rated on a 5-point scale. Football context, not abstract psychology.

⚙️Step 02

Scores calculated

Raw responses weighted, reverse-scored where needed, and normalised to 0–100 per trait.

🤖Step 03

AI profile generated

Claude model generates archetype, narrative, strengths, and development areas from the five trait scores.

📊Step 04

Coach dashboard updated

Profile, squad analytics, compatibility flags, and coaching guidance appear immediately.

The Foundation: The Big Five Personality Model

FPT is built on the Big Five personality model — also known as OCEAN or the Five-Factor Model — the most extensively validated framework in personality psychology. Paul Costa and Robert McCrae's Revised NEO Personality Inventory (NEO PI-R, 1992) is considered the gold standard instrument, and subsequent research has validated the model's applicability in elite sport contexts including football.

For a full explanation of the model's origins and what each dimension measures, see What Is the Big Five Personality Model?


The Questionnaire

Players complete a questionnaire of football-specific questions, each rated on a five-point scale from “Never” to “Always.” Questions are designed to feel like football reflection — players describe how they tend to behave and feel in specific on-pitch and training situations, not how they think they should respond to abstract psychological statements.

Approximately 30% of questions are reverse-scored: for these items, a higher response value indicates a lower trait score. This is standard practice in validated psychometric instruments — it prevents “yes-saying” bias and improves measurement validity by ensuring respondents engage with each question individually.

Research basis: Stoll, Lau & Schmid (2010) validated the NEO Five-Factor Inventory structure specifically in elite football players — confirming reliability (ICC 0.86–0.91 over six weeks) and good factorial validity across all five dimensions.


How Scores Are Calculated

Raw responses are aggregated and normalised to a 0–100 scale per trait. A score of 50 represents an approximate population midpoint. Scores above 65 or below 35 are where the data becomes more meaningfully directional. The five dimensions are fully independent — a player's score on one trait does not influence any other.

In the coach-facing dashboard, traits are displayed using coaching-language labels rather than academic terminology:

Openness to ExperienceCreative Thinking
ConscientiousnessWork Ethic
ExtraversionCommunication
AgreeablenessTeam Mentality
NeuroticismPressure Response

Players never see the term “Neuroticism.” The renaming is not cosmetic — it reflects the applied reality that emotional sensitivity under pressure is a coaching challenge and a development opportunity, not a clinical characteristic.


Profile Generation

Individual player profiles — archetype, narrative, core strengths, and development areas — are generated by AI (Anthropic's Claude model). The player's five trait scores, their age, and their playing position are passed to the model, which generates personalised narrative grounded in the psychology of their specific profile.

Every trait position has genuine strengths. Framing is consistently developmental — not evaluative.

🧒

For under-17 players, profiles include explicit framing that traits are actively developing during adolescence.

🔒

Individual question responses are never shown to coaches. Only the generated profile output is visible — the archetype, narrative, and trait scores.


Player Compatibility Scores

The compatibility (gel) scores between players are calculated using an algorithm grounded in Allen, Greenlees & Jones (2013) and the team cohesion meta-analyses of Peeters et al. (2006). The score weighs four factors, with weightings reflecting the relative predictive strength of each trait in the published literature:

Conscientiousness similarity30%

Most disruptive source of intra-squad friction when it varies

Mean Agreeableness25%

High mean predicts cooperative, low-conflict pairing

Mean Emotional Stability25%

Shared composure under pressure is a strong cohesion predictor

Extraversion compatibility20%

With asymmetric penalty when the lower-E player is outnumbered

These scores are tendencies, not predictions. Two players with a low score may have an outstanding working relationship built on shared experience, mutual respect, or a specific complementarity that no algorithm can detect. The scores are a prompt for coaching attention, not a verdict.


The Squad Health Score

The squad health score is a composite measure of how well-balanced the squad's collective personality profile is for team performance. Component weights mirror the effect sizes from Piepiora & Piepiora (2021):

Pressure Responsed = 1.81
35%
Work Ethicd = 0.72
25%
Communicationd = 0.55
20%
Team Mentalityd = 0.38
12%
Creative Thinkingd = 0.47
8%

The score is a useful heuristic for identifying where squad-level personality composition may be creating collective vulnerability. It is not a performance prediction.


Data, Privacy, and UK GDPR

🇬🇧

UK GDPR compliant

All player data processed in accordance with UK GDPR. Under-16s require parental consent.

🔒

Raw data stays private

Individual question responses are never accessible to coaching staff — only generated profile outputs.

🗑️

Right to deletion

Players can request deletion of their data at any time, cascading across all associated records.


What FPT Cannot Tell You

We want to be direct about this, because the responsible use of personality data depends on understanding its limits.

Cannot predict match performance

Personality traits predict long-term developmental patterns — not whether a player will perform well on a specific day.

Cannot assess playing ability

A profile tells you something about how a player engages with training and responds to pressure. It tells you nothing about their technical level, tactical understanding, or physical capacity.

Cannot substitute for direct observation

A coach who has worked with a player for two seasons knows things that no questionnaire will reveal. FPT complements that knowledge — it does not replace it.

Not a clinical tool

A high Pressure Response score is not a mental health assessment. If a player appears to be struggling with their wellbeing, involve your club's welfare officer or a qualified professional.


Try FPT With Your Squad

Free to try. Coaches can set up a squad, invite players to complete the questionnaire, and begin exploring personality insights with no commitment required.

Start with your squad →

Further Reading

References

  • Allen, M.S., Greenlees, I., & Jones, M. (2013). Personality in sport: A comprehensive review. Journal of Sports Sciences, 31(16), 1783–1795.
  • Costa, P.T., & McCrae, R.R. (1992). Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO-FFI). Psychological Assessment Resources.
  • McCarthy, P. (2024). The hidden psychology in football: What pro players won't tell you. drpaulmccarthy.com.
  • Peeters, M.A.G., Van Tuijl, H.F.J.M., Rutte, C.G., & Reymen, I.M.M.J. (2006). Personality and team performance: A meta-analysis. European Journal of Personality, 20(5), 377–396.
  • Piepiora, P., & Piepiora, Z. (2021). Personality determinants of sports championship in team sports. International Journal of Environmental Research and Public Health, 18(11), 5733.
  • Stoll, O., Lau, A., & Schmid, K. (2010). Assessing the NEO Five-Factor Inventory reliability and factorial validity in elite football. Sportwissenschaft, 40(4), 255–264.