Summary
In this study I analyse the consequences of parking the bus and examine game state dynamics in general. I consider how leading teams have different ambitions to a tied or trailing team and analyse extensively how performance differs in tied game and 1 goal games. I introduce an idea to quantify loss aversion by comparing the performance of teams who see a draw as a loss to the performance of teams who likely see a draw as a more comfortable result. This leads me to the conclusion that parking the bus is a viable option. Total goal suppression offsets performance degradation enough to render parking the bus an ev-neutral strategy. Isolating deliberate tactics from psychological effects and weak performance are particular challenges.
Introduction
The phrase ‘Parking the bus’ has long had equivalents phrase with Spanish/Portuguese speakers but became popular in English speaking football due to comments José Mourinho made early in his career.
Game state (the current scoreline) exerts various influences on football games. Motivations shift, play styles shift, outcomes shift. Psychological human factors can also play an important role.
I was encouraged to make a study on the topic after reviewing two recent papers on the topic:
“The Impact of Formations on Football Matches Using Double Machine Learning. Is it worth parking the bus?” https://arxiv.org/pdf/2602.16830 by Ruiz-Menarguez and Badiella
The authors analysed performance across various tactical formations concluding that “there is no statistical evidence that ‘parking the bus’ offers any scoring or winning advantage”. They acknowledged that same formation could include different tactical behaviours. Furthermore, team quality can make the data noisier. If stronger teams favour similar formations, the formation may receive credit for performance that is driven by superior player quality.
“Parking the bus” by Guan, Cao and Swartz https://www.degruyterbrill.com/document/doi/10.1515/jqas-2021-0059/html
The second piece by Guan, Cao and Swartz concludes that teams sit deeper when leading and this results in conceding more shots.
I have worked on predictive team ratings for almost a decade and game states have been a particular area of interest for me. I did not want game state effects to weaken the predictive nature of my ratings so have tried various methods to adjust for game state. I hope this experience puts me in a good position to consider the question posed by the title of this study.
Part 1 – How does a leading team maximise their win chance?
When a team holds a one-goal lead, a draw in the remaining minutes will secure a victory. As low scoring football games have a significantly higher draw chance it follows that leading teams benefit from a low scoring game.
A lot of this study has been focused on 1 goal games (either team is leading by 1 goal) from the 70th minute until the end of the match. This late game window and scoreline is one of the most extreme game state scenarios and comes with appropriate sample sizes.
To show that a component of success for a leading team is low scoring totals we can use the Poisson distribution.
Firstly, let’s see how the win % of leading teams change if we hold the expected goal (xG) ratio at 0.5 (an even share of chances) and lower the total match xG.
Figure 1
Play after the 70th minute accounts for around 25% of the match. The first few rows of this table are realistic, the last few less so.
Figure 1 shows if a team leading by 1 goal concedes 0.5xG and creates 0.5xG after taking the lead, they will win the match about 75.6% of the time. However, if they could control the game such that they only create 0.3 xG and only concede 0.3 xG this win% grows to 83.0%. The expected points growth for these same numbers (2.48 > 2.63) is worth over 2 pts a season if the team finds themselves in a similar situation 15 times a season.
If a team creates more xG than they concede, this also helps them win the game:
Figure 2
For the first 5 xG scores I fixed the total goals in the game and increased the goal ratio of the leading team. For the second five I increased the xG differential while keeping xG conceded level.
As you might expect, adding offensive xG while leading is less valuable than reducing xG conceded.
I included this part to emphasise a team’s objectives when leading by a goal. As a draw is a success, a positive xG differential/strong xG ratio is only 1 component of success – a low scoring match is another. It’s not correct to say a team is performing worse while ahead just because of a weaker xG differential, we should look at overall xG suppression as well.
Let’s now examine how real teams are playing at tied, and how they are playing at +1, for the whole game and after the 70th minute.
Part 2 – Game state analysis across 10 European leagues (total scoring rates)
I have analysed 10 leagues (the 2025/26 seasons of the Premier League/La Liga/Serie A/Bundesliga/Ligue 1/Eredivisie/Bundesliga II/Scottish League/Super Lig and the Primeira Liga) with data sourced from whoscored.com (powered by Opta)
The analysis in this part is split up by team ability. Having experimented with various angles before this write-up I felt there was more to be learned by this categorisation than others. I am using my own xG model that incorporates 33 zones and 7 shot types.
Figure 3
This table shows the total goals per game (the numbers are standardised to represent goals per a 90-minute game) in games between tied teams. Owing to the symmetrical nature of a tied game there are only 4 different columns of data here (as every minute played at tied for a big favourite is a minute played at tied for a big underdog).
Figure 4
This graph displays the data from figure 3.
The error bars are 95% confidence intervals.
All data points represent the total goals per game per 90 minutes of play.
Each data series has a minimum at the centre and maximums at the edge. This represents the fact that one team being stronger than the other is a component of increased goalscoring in football (one set of players is stronger/faster than the other). Also note how much higher scoring games are from the 70th minute onwards. I’m confident this is a psychological effect that sees players start the game in a more relaxed fashion (a team has no strong motivation to take an early lead) before finding more motivation and urgency/intensity as the game nears its finale.
The differences in xG and goals can be related to a specific xG metric. The xG metric I created uses 7 shot types and 33 zones. It does not include a big-chance adjustment for this study. Across all minutes, figure 4 shows slightly higher figures for goals compared to xG. This may be a small calibration issue, for example 100 xG with my xG metric may result in 102 goals. If we look at data points for the >70th minute however goals do have a margin over xG for all 4 bins used in this plot. A fancier xG model may not have showed this effect. This implies either the chances after the 70th minute are bigger than what my xG model sees or players are better at finishing after the 70th minute (most likely the former).
Next, we can look at the same parameters but for play where one of the teams leads by 1 goal.
Figure 5
The data will not be symmetrical around the central column like figure 4 as a strong team leading by a goal does not create an underdog team leading by a goal.
Figure 6
The error bars are large for heavy underdog teams leading by a goal as this is a rare occurrence, especially after the 70th minute.
This plot illustrates plenty of differences to figure 4. The 70th minute onwards is still the higher scoring portion of the match, but the gap is less significant – in fact the error bars almost overlap at times. Matches where the heavy favourite leads by a goal are not significantly higher scoring than when closely matched teams are playing a 1 goal game.
Figure 7
Figure 8
Figure 7 made a comparison with ratios while figure 8 plots the change in difference in total GPG. Positive y-numbers mean 1 goal games are higher scoring than tied games.
You may look at the black and pink series (goals and xG for all minutes) and conclude that 1 goal games are intrinsically higher scoring than tied games. This is arguably wrong as when we compare tied states vs. +1 states for ALL minutes, we are comparing different minutes of the game. Tied states on average exist much earlier in games than 1 goal games, and, as shown in figure 4, football games are much higher scoring late on in games (even for tied, closely matched teams). Comparing tied play in the 30th minute to 1 goal games in the 70th minute is comparing apples to oranges.
Play after the 70th minute removes this issue as we focus in on the same portion of the match for both game states. (I calculated that the average minute for 1 goal games after the 70th minute is only 40 seconds later than the average minute for tied games after the 70th minute.) In turn we then start to suffer more of an issue with sample size.
The plot shows evidence that strongly favoured teams play lower scoring matches when they are 1 goal ahead than when tied. As the team leading by 1 goal becomes less favoured the total match scoring increases.
Figure 9 shows the sample sizes involved in these plots
Part 2 – Game state analysis across 10 European leagues (performance & field tilt)
So far, we have looked at the total scoring of teams during tied play and during 1 goal games. 1 goal games appear somewhat lower scoring for a fixed minute, but it depended on how favoured the leading team was pre-match. Next, it’s relevant to consider the performance level of teams in these states (attacking xG/total match xG).
Figure 10
Better teams are better.
Figure 11
Similarly, strong favourites are much better when leading by a goal than strong underdogs. However, the dominance levels have dropped relative to tied. Using the log method I can compare these performance drops.
(I am using logs because the difference between 0.8 and 0.7 is more than the difference between 0.6 and 0.5. An 80% share of play is 4x more of the play than the opponent, while at 70% share is only 2.3x as much. Compare this to 0.6 and 0.5 which is 1.5x vs. 1x as much respectively.)
Figure 12
These are calculated as if the team’s play at +1 was matched up to their own play at tied.
The ratios are converted into logs using
The difference of the logs is then converted back into a ratio using
Performance drops are universal and more extreme after the 70th minute.
xG ratio drops a similar amount for teams of nearly all abilities. Underdog teams fare slightly better, but this is likely down to a) variance in the sample data and b) underdog teams are in a more similar position drawing or up a goal. Underdog teams and their opponents (after the 70th minute) may see both scenarios in a similar way (a draw is a good result as an underdog but not for closely matched teams or favoured teams).
I can use the Poisson distribution to calculate the win % change for tied play after the 70th minute compared to +1 play after the 70th minute.
Figure 13
I used xG ratios and total xG to calculate these figures. For example, if a team’s play at tied (>=70th minute) had an xG ratio of 0.6 and the game had a total xG per 90 of 4.0 this would be an xG score of 0.6 – 0.4 (>=70th minute). If at plus one they had a ratio of 0.4 and total xG per 90 of 3.5 this would be an xG score of 0.3 – 0.525. The success rate is then the draw % plus the win % of these xG scores computed using the poisson distribution.
The lower scoring nature of some play at plus 1 is not enough to counter the significant degradation of performance for team in every category. This means teams leading by a goal suffer worse outcomes than if their share of play at tied was replicated.
Those familiar with modelling games using the Poisson distribution should not be too surprised with these results as this is the main reason why draws are more common than the distribution predicts (all games are biased to return to a drawn scoreline).
Very dominant teams see the smallest reduction in success rate owing to their games becoming significantly lower scoring. It may be that the strongest teams are playing the situation of a 1 goal lead the smartest but the findings in could also be due to a psychological component. Its possible big underdogs don’t have the belief they can equalise while when the weaker teams lead, they face a particularly strongly motivated opposition (desperate to avoid a shock loss). Struggling to separate psychological/more subconscious factors to deliberate tactics is a common theme in this study.
Field Tilt
If we want to answer the questions of ‘should teams park the bus?’, we need to relate performance to field tilt. A defensive field tilt (many more passes conceded in the defensive final third than attained in the attacking final third) is approximately what people mean by ‘parking the bus’. Another factor to consider is how lower field tilts also result from ‘bad play’. E.g. a team is getting dominated, getting tired e.t.c
If a team has a ‘tilt’ above 0.5 that means they are passing more from the attacking third than their defensive third.
Figure 14
In a similar vein to figure 10, good teams play more in offensive areas. The better teams see a more attacking field tilt the longer the game remains at tied.
Figure 15
Comparing the difference using the previously described log method:
Figure 16
Relative to their play at tied, all teams see a similar field tilt change towards the end of games. However, less favoured teams start the change earlier. The most strongly favoured teams only changed their play style significantly from the 80th minute on. Underdog teams see a strong field tilt change as soon as they score, even if it’s in the first 15 minutes of the game.
Figure 17
Performance at +1 matched up to performance at tied (0.5 = play is the same, <0.5 means play at +1 is worse)
The xG ratio changes do not strongly match up with field tilt changes from figure 16. While xG ratio deteriorates for all teams through the game (at +1 compared to tied), it does not correlate with the early style changes from underdog teams. The xG ratio change is more strongly correlated with minute than it is field tilt.
How do strong teams lower scoring
I found a strong correlation between total scoring and a passing metric. The following compares the total final third passes in the match at +1 to the total final third passes at tied. A lower total therefore implies more play is taking place in the middle of the pitch.
Figure 18 – data points below zero mean there is less play in either final third in 1 goal games vs. tied games
This is quite intuitive; more play further away from the goals = less goals. When they lead the strong teams can control the match better in the middle of the pitch and the ball spends less time in final third. Weaker teams lose some control, and we see more end-to-end action when they lead by 1 goal than when tied (the field tilt changes I showed previously illustrates this is not because they are defending deeper).
We have seen teams leading by a goal suffer quite heavy performance degradation (by xG and goals) regardless of how favoured they were to win. This is fairly in line with significant field tilt changes through the game. Leading teams that are at least equal in strength to their opponents do play lower scoring games when leading by a goal (which helps win % on its own). I showed pass numbers that demonstrate this is probably because they can control the ball better in the middle of the pitch.
Overall, however, teams of all abilities would have more success if they could replicate their play at tied. Does this mean their tactics when they lead by a goal must be flawed?
Part 4 – Loss aversion
Loss aversion is an important aspect of both life and sports. Daniel Kahneman and Amos Tversky were key researchers in the late 20th Century credited with the formalisation of the idea. They calculated that humans are approximately twice as motivated to avoid a loss than achieve a win. This is key for losing game states as losing teams will naturally be able to find more motivation than the protecting/leading team.
It is a challenge assessing how to quantify this in football terms. We have seen in the last part that leading teams suffer a significant performance hit but how do we know whether to assign this to loss aversion or the tactics they employ. Without being able to separate the two it is hard to answer the title of this study.
I had an idea to demonstrate a clearer example of loss aversion by looking at tied play. I think we can see a similar effect to 1 goal games in tied games specifically when strong teams are playing weaker opposition. In said situation I think strong teams will see the draw as a loss (2 points lost) and experience loss aversion as full time starts to approach.
This means we can look for a loss aversion effect by comparing how strong favoured teams play at tied compared to their less favourited counterparts.
Firstly, the following shows play at tied between teams that are closely matched:
Figure 19
Figure 20
‘Closely rated teams’ are teams that are rated within 0.2 goals per game of each other.
The 10 leagues used for analysis are the same as in the previous part (over 3,000 games).
The table shows matches continually becoming higher scoring the closer we get to full time. This is constrained for game state and constrained for similarly matched teams so is an accurate barometer of how goals increase through football games. We could have hypothesised that closely related teams may be happy to settle for a draw late on in games, but this is not borne out by the data.
Figure 21
The numbers are relative to the 90-minute average GPG for each category (figure 19). The (avg GPG) row at the bottom shows the averages. For example, 1.54 on the xG table (left) means big mismatches (one of the teams is expected to win by at least 1.4 goals) are 54% higher scoring after the 90th minute than the game average.
I am going to treat the numbers between closely matched teams as our ‘control group’. The idea is neither team sees a draw as a loss so no loss aversion is present for these games.
Comparing the xG and goal numbers against this control group gives:
Figure 22 – Does this figure demonstrate loss aversion?
The data shows teams that heavily favoured teams play higher scoring games as the clock nears full time.
We also should consider the performance level of the favoured team:
Figure 23
The numbers are standardised such that we could say if a dominant team (expected to win the game by at least 1.4 goals) in a tied game state after the 90th minute played itself between the 60th and 70th minutes (also at tied) said team would achieve a 60% xG ratio (0.64 matched up against 0.53). The 0.85 value for goals may be related to a small sample size. This figure comes about from a goal difference for this data point of 20 – 1. This 0.952 ratio is then standardised against an average performance ratio of these teams when tied of 0.800. Using a logit transformation method this results in the 0.85 seen in the table.
This shows more favoured teams increase their performance level very late in games, increase the goal conversion rates (compare xG and goals in the previous figures) and play higher scoring matches.
Lastly, it’s useful to consider the field tilt changes as well:
Figure 24
The field tilt changes more through a game between increasingly mismatched teams.
I think all these tables represent loss aversion because each column represents teams with extra reason to view a draw as a loss (a single point is nearly always a bad result if you are so favoured to win the game).
In the first section I was focused on comparing play after the 70th minute in 1 goal games to the same time frame in tied games. Using the data above I calculate the following ‘loss aversion’ factors for the 70th minute onwards:
Figure 25 – Heavily favoured teams find this much extra performance after the 70th minute (I averaged results for 70th minute onwards from figures 22, 23 and 24) . These numbers compared to their own performance for the entirety of the match and relative to how these metrics changed throughout games between closely rated teams.
I believe these values go some way to quantifying loss aversion as they denote the extra performance a team finds when they begin to fear dropping points.
An issue with this method for assessing loss aversion may be that the lower ranked team is a not a consistent entity with a similar motivation for a draw for each level of mismatch. This is unlikely to be completely true as teams that go in as heavy underdogs may be more motivated to protect a draw as closely matched teams. However, seeing as I used the word ‘protect’ here, they are not in loss aversion, unlike the heavily favoured drawing team that do not see a draw as something to ‘protect’.
It now becomes more apparent that strongly favoured teams (figure 8) lower the total scoring of a match in a comparison that is not equivalent to the less strongly favoured. After the 70th minute when tied, these teams will feel loss aversion because a draw is a bad result. This increases the total scoring at tied which results in 1 goal games being relatively lower scoring.
Considering how teams of differing ability profile the same in terms of pass ratio, xG ratio and (with extra adjustments) total GPG as well I am not sure separating teams by ability reveals as much extra information as I initially thought it might.
I want to compare the values from figure 25 to earlier figures 10, 16 and 17 that analysed the play of teams that lead by a goal late on in games. Averaging the numbers across teams of all abilities gives the following stats for trailing teams:
Figure 26
xG and goal totals are close to 1 which means 1 goal games after the 70th minute are neither higher or lower scoring than the equivalent period of tied games. However, given 1 of the teams will be experiencing a strong loss aversion, the fact total goal rate does not increase is important. Loss aversion numbers from figure 25 shows we would expect an increase of around 10%. This makes sense as we would expect a more end to end game – the trailing team will often leave space in defence in a desperate attempt to find an equaliser. The tactics of the protecting team must be supressing total goals/xG.
xG ratio and goal ratio see a large increase for the trailing team but some of this is because of improved performance from the trailing team owing to the loss aversion they experience. The goal ratio figure from figure 25 is subject to some uncertainty.
The field tilt/pass ratio of trailing teams is far beyond the loss aversion figure. This implies the protecting team is making a deliberate effort to defend deep as loss aversion alone is unlikely to create such a large change in field tilt.
Figure 27
If we take the xG ratio the win rate of teams drops by ~4.4% but if we take goal ratio as more accurate it drops a miniscule ~0.3%. The calculation here is effective for a team that takes the lead in the 70th minute. I have not found evidence that parking the bus is a powerful and beneficial tactic but there is evidence here that the suppression of total goals (increases draw % for the remaining portion) and the relatively small decrease in performance make it a viable strategy.
Part 5 – assorted analysis angles
League by league
It could be revealing to compare team’s play in different countries to see if the philosophy changes.
Figure 28
The big 5 leagues all seem very similar play between teams. Some other leagues see teams appearing to play slightly less defensively when leading after the 70th minute. I found a weak correlation that this did result in an inability to supress goal totals but this correlation was solely down to the Scottish data point (1 goal games after the 70th minute in Scotland were high scoring).
The consistency across the leagues (especially the big 5 leagues) is evidence player psychology could be as much a driving factor for field tilt change as managerial choices (otherwise we might see more differing philosophies between leagues).
Team by team
I analysed team by team to see if teams that saw smaller/larger field tilt changes saw differing performance figures (again after the 70th minute and comparing tied play to +1 play). I found a correlation that a less defensive field tilt saw better performance (not a big surprise as an attacking field tilt is also an indicator of playing well) and a weak correlation that a less defensive field tilt saw high scoring games (bad). I decided to stop pursuing this line of analysis as the error bar of each data point was too high to work with. This could be mitigated in the future by a very large sample of teams.
General team play
I correlated field tilt for each team to overall ability to see which teams are unexpectedly defensive or attacking.
I found a weak correlation (r^2 of 0.02-0.05) that the more defensive teams played lower scoring matches.
It could be interesting to use player values to see if the teams that are more defensive/attacking (in terms of field tilt) than would be expected for their level have higher or lower than expected performance levels given their player values. I hypothesise playing defensively may be a smarter option but maybe that is biased by high profile teams like Atletico Madrid over the last decade.
Part 6 – Conclusions
I have shown how play changes between drawn games and 1 goal games.
A common theme of the study has been psychology. A lot of the results we have observed for the different game states for teams of different ability and can be explained by well-known psychological effects such as loss aversion.
I have found limited evidence of managerial tactics as psychological dynamics can explain most differences between play at different scorelines.
I have not found evidence that parking the bus helps teams achieve victories, but I also showed that it is very close to not hurting the performance either. Quantifying the loss aversion effect is a challenge so this conclusion could be subject to change in the future.
It could be naïve to look at data and say what players should/shouldn’t do. Telling players not to sit deeper with a 1 goal could force unnatural behaviours that harm performance.
Managers should be flexible, consider their team’s strength and learn from past results. If a team is strong at defending crosses (most strong defences are) then intentionally sitting deeper could be very valid (crosses are more frequent in this situation). This is something that could change on a team-by-team basis. Managers should be familiar with loss aversion and incorporate it into tactical decisions. For example, how motivated are we to take an early lead?
If you have enjoyed this study, please consider subscribing to my blog using the white box on the top left of the main page at syzygyanalytics.co.uk. If you have any ideas suggestions for further alternative methodologies or criticisms of what I have written please get in touch! You can message me on x.com/samh112358 or syzygyanalytics.bsky.social
References
- Guan, C., Cao, J., & Swartz, T. (2021). Parking the bus. Journal of Quantitative Analysis in Sports, 17(3). Link
- Ruiz-Menarguez, M., & Badiella, L. (2026). The Impact of Formations on Football Matches Using Double Machine Learning: Is it worth parking the bus? arXiv pre-print. Link
- Opta match data via WhoScored.com
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