40 Questions Sports Organisations Should Ask Before Trusting AI
AI tools like Catapult, Second Spectrum, and Hudl process thousands of data points faster than any human ever could, but they cannot see what your scout saw in a young athlete's composure or what keeps your fan community coming back. Your organisation's competitive edge depends on knowing when to trust the algorithm and when to trust the person in the room.
These are suggestions. Use the ones that fit your situation.
1When Catapult AI flags a player as fatigued, does your coaching staff know whether the algorithm measured only physical load or also accounted for the psychological stress of playing against a rival team?
2Second Spectrum identified a tactical pattern in your opponent's play. Can you name the human analyst who confirmed this pattern exists before you changed your game plan?
3Your Hudl AI system recommends pulling a player from a match due to injury risk metrics. What happens to that player's confidence and mental state if you bench them based on a probability score?
4When you use performance data to make selection decisions, are you measuring only what happened or also why it happened in that specific match context?
5Sportlogiq's movement analysis shows a midfielder covered less ground than usual. Does your system distinguish between a player conserving energy intentionally and a player who is genuinely struggling?
6Your AI tool recommends a training drill because it optimises a specific metric. Who decided that optimising this metric actually makes your team better at winning matches?
7When Whoop AI suggests increased recovery time, can your medical team explain whether this recommendation is based on that specific athlete's physiology or on patterns from athletes in a different sport entirely?
8An AI system ranked your players by performance score this week. Does that ranking reflect what your coaching staff observed in training, or has the algorithm identified something real that contradicts their assessment?
9Before you act on an AI recommendation to change an athlete's training load, do you know whether the tool accounts for the athlete's personal motivation to play in an upcoming fixture?
10Your performance analysis AI shows a decline in a key metric for your star player. How will you find out whether this is a genuine performance issue or a normal variation you would ignore if you were watching live?
Scouting and Athlete Development
11Your scouting AI identified a player with elite sprint speeds and passing accuracy. Does it have any way to measure whether this player wants to be a professional athlete badly enough to sacrifice the other things their peers enjoy?
12An AI system recommended signing a young player because their physical attributes match your club's profile. Who watched this player compete under real pressure, and what did they see that the algorithm could not?
13Your development programme uses AI to assign training content to young athletes. Can you explain why a 16 year old with different personality traits should follow the same progression as another player with identical physical measurements?
14A scouting AI flagged a player from a lower league as a prospect. Does your organisation have a process for scouts to explain why they disagree with the algorithm's assessment before you pass on a player?
15When you use AI to identify talent gaps in your academy, are you measuring the players you need to win in three years or the players you need to win right now?
16Your AI tool ranked young players by potential based on their current physical profile. What happens to players who mature late or who showed late bloomers in their family history?
17An algorithm recommended cutting a young player from your development squad because their progress rate fell below a threshold. Does anyone on your staff remember another player who recovered from this exact situation?
18Your scouting system identified technical weaknesses in a prospect's game. Before you reject them, can an experienced coach tell you whether those weaknesses are fixable or fundamental to how the player thinks?
19AI analysis shows a player's decision making speed improved this month. Do you know whether they actually got better or whether they simply played against weaker opponents who gave them more time?
20When your development AI recommends specialising a young athlete in a single position, who verified that removing positional flexibility actually serves their long term career and your squad's future needs?
Fan Engagement and Community
21Your fan engagement AI recommends showing highlight reels of attacking plays to increase video watch time. Does this actually build the community feeling that makes people buy season tickets, or does it just optimise for clicks?
22An algorithm personalises each fan's experience based on their viewing history. How will you know whether you have created 10,000 individual content streams or one community watching the same match together?
23Your AI system predicts which fans are most likely to stop attending matches and recommends targeted promotions. Are you solving the problem or just treating the symptom?
24Fan engagement data shows that younger audiences prefer short clips over full match analysis. Before you restructure your content strategy, did you ask whether this preference exists or whether it was created by the algorithm itself?
25Your recommendation engine suggests match day content to fans based on their past behaviour. Who decided that giving people more of what they already like builds a stronger fan base than exposing them to unfamiliar players and tactics?
26An AI tool identified that supporters engage more with emotional player interviews than tactical breakdowns. Does this insight change what content you should create, or does it reveal a gap in how you have explained the sport to your audience?
27Your system tracks fan sentiment in real time during matches using social media analysis. When it reports that sentiment is declining, can you tell whether fans are actually unhappy or whether the algorithm is misinterpreting sarcasm and rivalry banter?
28An algorithm recommends which club stories to feature based on engagement metrics from past months. Does this leave room for the unexpected story that nobody predicted but that reminds people why they love your club?
29Your personalisation AI learns what content each fan prefers and delivers more of it. How will you prevent long term fans from being algorithmically pushed away because their interests do not match the current engagement curve?
30Fan engagement data showed an increase in interaction when you changed your social media posting time. Before you automate this change, did anyone verify that you were reaching the right people or just reaching more people who do not actually attend matches?
Organisational Culture and Decision Authority
31Your coaching staff receives an AI recommendation about team selection. Does the recommendation come with an explanation of the reasoning, or is the decision presented as a single number you either trust or reject?
32An AI tool suggests that your current team culture metrics are declining. Before you implement recommendations from the algorithm, can a senior leader articulate what your team culture actually is?
33When an AI system flags a player's behaviour as a risk factor, who on your staff has the authority to override the algorithm based on knowledge of that athlete as a person?
34Your organisation uses metrics to evaluate coach performance. Does this system account for the coach's impact on areas you cannot measure, like a player's willingness to chase a losing cause?
35An algorithm identified that a particular training drill produces the best measurable outcomes. Before you make it mandatory, did you check whether players resent the drill so much that they perform worse on match day?
36Your performance management system uses AI to track individual athlete metrics and highlight underperformers. How does this system distinguish between a player having a difficult week and a player developing a longer term problem?
37An AI tool recommends changes to your organisation's structure based on efficiency metrics. Who is responsible for preserving the informal networks that actually get things done in your club?
38Your staff receives an AI report showing which decisions led to winning outcomes. Does the report account for the decisions you made that worked despite the AI saying they would not?
39When you use AI to allocate playing time or training resources, how do you ensure this does not become the automatic justification for every selection decision, replacing the coach's authority to explain their reasoning?
40Your organisation invested in an AI system that your most experienced leader strongly doubts. What process allows that leader to be heard before the technology becomes embedded in how you make decisions?
How to use these questions
Always ask who made the decision to trust this metric in the first place. Most AI recommendations optimise for something someone chose to measure, not for what actually matters in your sport.
Create a log of AI recommendations your organisation rejected and the outcomes. This forces your staff to articulate why they disagreed and whether they were right.
Require any AI recommendation that affects athlete welfare, selection, or development to be presented with a human explanation of the reasoning before it is acted upon.
Test your AI tools by running them on match video from five years ago and asking whether the system would have made the same decisions your organisation made. If not, understand why before you trust it today.
Protect the role of the person in your organisation who says no to the algorithm. This person is not slowing you down. They are preventing you from outsourcing your judgement to a tool that cannot see what matters.