# Event Data vs. Tracking Data · Football Level 8: Expert

Football · Level 8: Expert · Insporta Book (https://book.insporta.app/football/level-8/event-data-vs-tracking-data/)

Event Data vs. Tracking Data in Football (Level 8: Expert) on Insporta: 100 sourced facts behind 10 tests. Covers: how event data is collected; and more.

How football data is made: logging events, qualifiers, coordinate systems, optical tracking, data quality, synchronisation, providers, open data, pitch control and which question needs which data.

## Test 1: How event data is collected

- Data companies employ analysts who watch the match and tag every on-ball action with its type, time and location. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Opta began in 1996, rating Premier League players with an index built from logged match actions. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Every pass, touch, duel and shot is an event, which adds up to thousands in 90 minutes. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Clients want numbers during the match, and a post-match pass fixes errors in locations and event types. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Knowing who did what, when and where is the core of event data; details come as extra labels. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- A run that drags a defender away leaves no event, because nobody touched the ball in that action. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Custom tools let a logger click the pitch location and pick the event type as fast as the game moves. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Speed comes first for live products; accuracy is added in the checking stage before the final release. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Reep began in the 1950s, Opta in 1996, and StatsBomb added 360 location snapshots in 2021. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Events carry details such as the body part used; the other options are not part of an on-ball event. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)

## Test 2: Event types and qualifiers

- Qualifiers add detail such as body part, pass height or whether the ball was a cross. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- A pass can be headed, long, a cross and under pressure all at once; qualifiers let it say all of that. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- The 'under pressure' flag marks actions taken with an opponent closing in, which changes how they should be judged. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- xG models treat a header from a spot very differently from a strike with the foot from the same place. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- What counts as a tackle, a dribble or a key pass varies, so the labels depend on each provider's rules. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Where a pass went astray matters for turnovers and counters, so the intended or actual end point is kept. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Passes are completed or not, duels won or lost, shots on target or off: the outcome records which. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- The type says what happened, the outcome whether it worked, and qualifiers add the finer details. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- The count is built from the basic event plus a qualifier and a location test. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Height, body part, pressure and type are qualifiers: extra descriptions added to the basic event. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)

## Test 3: Coordinate systems

- Opta's pitch is a percentage grid, so 50 on the length axis is halfway, whatever the real size of the ground. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- StatsBomb records locations on a 120 x 80 grid in yards-style units, not in percentages. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- A point at (60, 40) means the centre spot for one provider and somewhere else entirely for another. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Normalising direction means a location near 100 is always close to the opponent's goal. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- A pass across 50 units of width is a different number of metres on a narrow ground and a wide one. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Tracking systems work in real distances, commonly with the centre spot as the origin. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- The length runs from 0 to 120, so 60 is exactly halfway. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Opta measures length as a percentage, so higher x means closer to the goal being attacked. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Direction and scale have to match before any point can be compared. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Halfway along 120 and halfway across 80 gives (60, 40); (50, 50) is the centre on a 100 x 100 grid. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)

## Test 4: How optical tracking works

- Cameras placed around the roof cover the pitch from several angles, and software turns the images into positions. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- The software has to know exactly where each camera looks and how big the pitch is to turn pixels into metres. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- When bodies overlap, the system can lose or swap identities, which is why several angles are used. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- A small object moving at up to 100 km/h and leaving the ground is much harder to follow than a player. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Software identifies players from appearance and position, with operators correcting any swaps. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Hawk-Eye's camera systems are used for goal-line decisions and, increasingly, for player and ball tracking. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Automation does most of the work, but people correct the moments when the software loses track. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Detection comes before geometry, and identity is attached last so that every point belongs to someone. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- The more bodies overlap, the harder it is for the cameras to keep each player separate. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Three players close together block each other from any one camera, so views from other sides keep them apart. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)

## Test 5: Data quality and errors

- Agreement between independent loggers shows whether event definitions are applied the same way. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- A goal is a goal; whether a challenge was a tackle, a duel or a foul depends on the logger's reading. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- A logger judges where the action happened on screen, which is quick but not exact. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- An impossible jump between frames means the system lost him and found him again somewhere else. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Spot checks against video show whether the definitions and locations match what the club sees. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- A tighter definition of a tackle or a key pass lowers the counts without anyone playing differently. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- If an event happens twice a season, one mistake is half the total; common events absorb errors better. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Goals are unmistakable, passes mostly clear, and tackles depend on judgement. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- An audit needs a trusted reference, which the club builds itself before comparing. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- 18 against 13 for the same match shows that deciding what counts as a tackle is a judgement call. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)

## Test 6: Synchronising events and tracking

- Events say what happened on the ball; tracking adds the positions of all 22 players at that moment. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- A logger's timestamp can be a fraction of a second off, so the event must be matched to the right frame. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- The moment the ball's tracked position starts to move away from the player marks the real time of the pass. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- At sprinting speed a player covers several metres in half a second, so a mismatched frame puts him in the wrong place. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- 360 gives a snapshot around each event from broadcast video, not a continuous record of all players. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Event clocks restart at 45:00 while tracking runs on its own time, so each half needs its own alignment. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Knowing where team-mates and opponents stood shows which passes were available, not just the one chosen. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Clock alignment comes before event matching, and a video check confirms the join worked. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- An event is one action, a freeze frame adds visible players, and a tracking frame has everyone. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Aligning the event with the frame where the ball actually leaves the foot puts every player in the right place. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)

## Test 7: Providers and official league data

- Perform, which had bought Opta, merged with STATS in 2019 to create Stats Perform. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Hudl, known for video analysis software, added Wyscout's scouting platform and database. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Fast, reliable live data feeds broadcasts, apps and in-play betting, so leagues license it for a fee. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- One provider may be strongest on event detail, another on tracking, another on smaller leagues abroad. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Detailed logging and tracking cost money, so coverage is deepest where the paying customers are. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Broadcasters, apps and betting firms need the events almost instantly, which is what live feeds provide. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Detailed positional data on their own players and opponents is a competitive advantage. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Opta began in 1996, Hudl bought Wyscout in 2019, and StatsBomb launched 360 data in 2021. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Speed is the product: the whole chain runs in seconds. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Providers collect and check the data at the stadium and sell it on to clubs, broadcasters and others. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)

## Test 8: Open data sets

- StatsBomb's open data also covers women's football and several major tournaments, for learning and research. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- Metrica's anonymised sample matches give full positional data, which is otherwise very hard to obtain. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- The paper in Scientific Data released Wyscout events for European leagues and major tournaments. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- Camera systems are expensive and the data reveals a lot about teams, so little of it is released. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- Researchers and club analysts ran it on YouTube during the pandemic, using open data sets for lessons. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- Working with public data is how many analysts built portfolios that led to jobs in the game. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- Releases are usually selected tournaments or seasons, so they cannot answer every question about every league. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- The Wyscout paper came out in 2019, Friends of Tracking in 2020, and open 360 data from 2021 on. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- The same steps, done on public data, are how many analysts learned the trade. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- Free event data sets far outnumber free tracking data, so most public projects are built on events. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)

## Test 9: Pitch control and off-ball models

- From players' positions and speeds, it works out which side would reach a ball played to any point first. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- Spearman's paper modelled how likely off-ball positions were to lead to goals, using tracking data. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- Who controls a space depends on where all 22 players are and how fast they are moving. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- EPV rates every moment of a possession by how likely it is to end in a goal, so each action's effect can be measured. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- The Boston conference is where researchers and clubs share analytics papers across sports. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- A decoy run that drags a defender away increases his team's control of another area, which the model can show. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- Models assume set reaction times and speeds, which real players, tired or alert, do not always match. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- Positions and speeds come from tracking; the map shows which side owns each area at that moment. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- Basic xG needs only events, freeze frames add visible players, and pitch control needs full tracking. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)
- The home side controls the areas around 10 and 9, so passes into them are likely to be won. (Source: MIT Sloan Sports Analytics Conference papers and published data sets)

## Test 10: Which question needs which data

- A run without a touch leaves no event, so only continuous positions can show it. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Passes are on-ball events with start and end locations, which event data records directly. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- The line's height is a position held all match long, which only tracking records. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Shots against, with their locations and quality, are logged events. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Events record the tackles and turnovers; tracking shows the distances closed and the lanes cut off. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Events find the set pieces and the shots; video shows the marking and movement behind them. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Which way a player faced, whether he checked his shoulder and how he communicated are clearest on video. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- Shot counts are events, defenders at the shot need freeze frames, and pressing runs need full tracking. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- The question decides the data, not the other way round. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)
- A freeze frame records the visible players' positions at the moment of each event, including the shot. (Source: Data provider documentation (Opta, StatsBomb, Wyscout) and the analytics literature)

Sources: IFAB Laws of the Game / FIFA & UEFA technical reports / Opta, StatsBomb and FBref definitions / club and analyst publications / peer-reviewed research

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