The Impact of VAR on the Elite Game of Soccer

By Noah Hollander

April 29, 2020

Abstract

The purpose of this study was to contextualize and quantify the disruption caused by an

introduction of new technology on the pertaining industry. To study this, soccer data from each

of the top five leagues – Bundesliga, La Liga, English Premier League, Ligue 1, and Serie A – was

used for the seasons between 2014/15 and 2019/20. The technological advancement

implemented in these leagues was VAR. A generalized linear model using an ordinary least

squares method, which was also fitted with a Poisson regression, was used to determine a

causal impact VAR had or did not have on these leagues, while controlling for team fixed

effects. From the results, we see that the technology had a negative impact on home goals,

away goals, home fouls, total goals, and total fouls. away yellow cards, home red cards, and

away red cards. We can infer that technology does act as a disruptor in some way to the

industry at hand.

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Introduction

Technology has always acted as a disruptor to industries and economies as a whole.

This technology could come in many forms: from advancements like online banking to

technology for employee monitoring. Some benefit from these technological advances, while

others may come out worse off from them. In the financial services industry, Financial

Technology, or FinTech, has become a huge catalyst for online banking. Companies can also

use technology to moderate the workplace. In many industries, the neoclassical principal-agent

is always a key topic of discussion; how can a company monitor its manager and employees to

decrease shirking and increase profits? Although most see these advances as a benefit for the

particular industry, it does leave some straggling behind.

Companies may liquidate its assets with the introduction of smaller, tech companies

that are more effective and efficient. People end up losing their jobs because of the stress

induced by monitoring, or if their jobs could be automated by a technology to increase

profits and efficiency. To create a quantitative analysis of the impact of a certain technology

can have on the pertaining industry and the employees of that said industry or company, this

study was put in the context of soccer. The study examined if the introduction of the Video

Assistant Referee (VAR) had any impact, positive or negative, on goals scored and fouls and

cards given in a particular season.

Background

VAR was first introduced into Western European Professional, Elite Soccer in one of

the big 5 leagues – Spanish La Liga, Italian Serie A, German Bundesliga, French Ligue 1, and

the English Premier League – at the beginning of the 2017/18 season in the Bundesliga and

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Serie A. Th next season, Ligue 1 and La Liga followed. Lastly, the Premier League introduced

it at the beginning of the 2019/20 season. According to the website of the International

Football Association Board – which is the committee that partners with FIFA to determine

the Laws of The Game of soccer – VAR was introduced into soccer not to perfect the game

but to reduce referee error while also minimizing dangerous plays (“Video Assistant Referees

(VARS) Used Live In Competitions and Leagues,” 2019). This is why it can only be applied to a

game in four situations: goals/no goals, penalty decisions, direct red cards, and mistaken

identities in regard to yellow or red cards. The VAR, which is a team of match officials, can

inform the head-match official, who is refereeing on the pitch, that assistance or replay is

needed for one of the four situations described above. The head official can also ask the VAR

for assistance if the head official is unsure about the incident.

A concern with the introduction was how teams and fans would respond. An article

from DW, a news site from Germany, stated that fans had a negative perception toward VAR

and the addition was ruining soccer (Ford, 2018). However, in a recent BBC article, a survey

was conducted that showed the majority of fans think VAR is beneficial but needs

improvements (“VAR makes Premier League less enjoyable – YouGov survey,” 2020). The

purpose of this study was to quantify the effects VAR had on the teams and referees; did this

monitoring technology reduce human error and excessive aggression? A non-randomized

stepped wedge design found in a study by Hu (2018) et al in which they focus on the benefits

of randomized and non-randomized stepped wedge designs. Hu (2018) describes this design

as beneficial for researchers who want to assess the consequences, positive or negative, of a

certain input. An ordinary least squares method using a generalized Poisson regression

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model was adopted for this study. The number of cards – yellow and/or red – given, fouls

called, and number of goals scored for pre and post implementation were analyzed to

contextualize the effects of VAR. Because VAR was implemented a few years ago, little

research has been tested on its effects on the game of soccer. This allows for the opportunity

to try to build a foundation for further research on a topic that strongly correlates with real

world situations.

Literature Review

There’s always caution when implementing a new technology: how this technology will

affect managers, employees, or fans (in the case of sports). A non-sport examination of

monitoring technology implementation can be seen in Pierce (2015) et al who study the effects

of implementing monitoring on employee theft. The data, which was obtained from the

Hospitality division of NCR corporation, consists of five different chain restaurants amounting

to 392 locations. They use a difference-in-difference specification with ordinary least square

regressions to estimate the impact the implementation of the technology has on employee

theft. The results show that the implementation of the monitoring technology did decrease

employee theft.

Technology can also act as a disruptor in an industry. Buchak (2017) et al examine the

impact of fintech and shadow banking have on large banks ability to lend in the residential

lending market. They run regular ordinary least squares regressions comparing shadow banking

to fintech and then seeing how these not-regulated sources of lending emerging into the

financial services industry affect the lending capabilities of retail banking in that same

community. Their results show that the emergence of fintech has disrupted retail banking

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lending. The growth of fintech and shadow banking has decreased the amount of originating of

loans by retail banks. Surprisingly, this was one of the only scholarly papers on fintech’s

disruption of the banking sector.

Bradbury (2018) and Mills (2017) examine the impact of introduction of some new

technology on referee bias. John Bradbury uses a linear regression with binomial distribution

and logit link function and Poisson estimator to estimate his results because the dependent

variable, proportion of pitches called strikes, is a percentage bound by 0 and 1. He measures

the effect of this new technology, QuesTec, on the percentage of pitches called strikes by

umpires. His main dependent variables are measures of plate-appearance outcomes, focusing

on called strikes and number of swings. The results are consistent with similar research that

finds that including QuesTec, a video monitor technology, in the ballpark does decrease the

number of strikes called by umpires.

Mills (2017) also examines the effects of the introduction of QuesTec while also

examining the impact of a Zone Evaluation system that would send reports to umpires after

every game detailing their performances. He uses a piecewise regression with a difference-

in-difference model to be able to examine the impact these technologies had on umpire

performance. Mills finds that accuracy of calls increased after the implementations;

however, one should be careful when implementing a monitoring system because of the

stress it can cause to the employees.

The only study found to examine the impact of VAR was Lago-Peñas (2019) et al., who

run a linear model and pairwise z-test to quantify how VAR affects soccer. They are analyzing

fouls, goals, total playing time, offsides, and penalties. This paper finds that after the

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introduction of VAR, there was a decrease in the number of fouls and yellow cards but an

increase in the number of minutes added to the game. While the foundation of the paper does

provide information on models to use to compare before and after, the models are not

intuitive, and the paper fails to provide reasonings behind the models used and equations

used.

While existing research does examine the introduction of technology in a specific industry,

there is a lack of research depicting the effects of introducing new technology on the way a

sport is played. Lago-Peñas (2019) does provide this type of research, but only for the German

Bundesliga and Italian Serie A for the 16/17 and 17/18 seasons. And while research from the

likes of Mills (2019), Pierce (2015) et al, and Buchak (2017) et al did provide a good foundation for

analyzing a pre and post implementation, their subjects were broader. This research will perform

an OLS model fitted with a Poisson regression that will compare the effects of introducing VAR on

number of goals scored, yellow cards, and fouls in games played before and after its

introduction. Unlike Lago-Peñas, this research will include data for all five big leagues. The goal

of this study is to be able to provide a framework for future research on a similar topic, while

also observing a difference in play for before and after VAR was introduced.

Data

The data being analyzed for this study derives from football-data.co.uk website. This is

an official UK betting website that provides game-level, historical fixtures, results, statistics, and

betting odds that are publicly available. The data from football-data.co.uk contains all results

and statistics from those games for the five, elite leagues – German Bundesliga, Italian Serie A,

English Premier League, French Ligue 1, and Spain La Liga – for the season starting in 2014/15

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and continuing through this year’s season, 2019/20. These specific five leagues were chosen

because of the plethora and consistent data found for all of them. Articles from BeSoccer and

other sports news sources ranked these five as the top five soccer leagues in the world (“The

top 10 Best supported leagues in the World,” 2020). The data type is panel data. The condensed

descriptive statistics for each league and combined statistics can be found in the Appendix. A

detailed guide on obtaining the data can also be found in the Appendix.

. The dependent variables of interest that are being analyzed from the tables are home

goals, away goals, home fouls, away fouls, home yellow cards, away yellow cards, home red

cards, and away red cards. Total goals, fouls, and cards were also added to the data to be able

to visualize a total effect if there was one. Two separate variables were created manually to

inform what season and what league were being analyzed. The season variable name is season

and can be denominated by a value from 2014 through 2019.1 The league variable name is div

and corresponds to one of the five leagues – Bundesliga, EPL, LaLiga, SerieA, and Ligue1. A

dummy variable, labeled VAR, was also created to examine the magnitude of the effect of the

technology on the dependent variables of interest.2 Dummy variables for each home team and

away team were also created to account for differences of play between teams. The unit of

observation is game-level, so each row details a single game between a home and away team.

On the next page is Figure 1, which is a snippet of how the data is formatted.

The main objective at this point was to show a causal relationship between the

introduction of VAR and the number of fouls – whether it be foul, yellow, or red card – and

1 If season equals 2014 then it is for the 2014/15 season; season equals 2019 when the 2019/20 season is being

observed

2 VAR variable will equal 1 if the league was using VAR at that point in time and equals 0 otherwise.

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later on goals scored. Figure 1 gives the density of home fouls in the Bundesliga in the six

seasons being analyzed. As one can see, it does seem that after the introduction of VAR, in

graph (4), the density of home fouls starts to decrease and skew towards zero. This is

consistent with the hypothesis that the introduction of VAR will disrupt different outcomes of

soccer while acting as a monitoring system and decreasing all dependent variables of interest.

Figure 1

To further visualize the decrease in fouls that’s being hypothesized, Figure 2 and 3 plots

home fouls, away fouls, home yellow cards, and away yellow cards over time for each league.

For these graphs, home and away red cards were dropped because of the lack of red cards

given in a season compared to fouls committed was apparent; the line for red cards was at 0 or

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close to it for all seasons. As expected, it seems that the number of fouls and cards does

decrease in the Bundesliga but could be a time trend. Regarding La Liga, it is hard to explain

whether the introduction of VAR had any effect on the league. Unfortunately, the lack of

seasons with the use of VAR does inhibit this study – this can be seen in the regression tables in

the results section.

Figure 2

Figure 3

0

1000

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6000

2014/15 2015/16 2016/17 2017/18 2018/19 2019/20

Bundesliga

Home Fouls Away Fouls Home Yellow Away Yellow

VAR introduced

0

1000

2000

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5000

6000

2014/15 2015/16 2016/17 2017/18 2018/19 2019/20

La Liga

Home Fouls Away Fouls Home Yellow Away Yellow

VAR introduced

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In Figure 4, one can see that the distribution of home goals is highly skewed. This is the

case with all of the dependent variables of interest. Because the data is count data, it doesn’t

go negative or past 0. A Poisson regression is used to account for this distribution.

Figure 4

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On this page is Figure 5, which is a snippet of how the data is formatted.

Figure 5

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Methodology

To estimate whether the introduction of VAR in each of the five leagues disrupted the

game, an Ordinary Least Squares model fitted with a Poisson regression will be used. This

model compares pre and post data to understand if the implementation of VAR affected some

outcome. The main regression is:

Log(Outcomeit) = ζ + it + it+ t + 1(VARit) + it

where i denotes one of the leagues; t is the time date of the game to signify either before or

after the introduction of VAR; Outcome denotes the number of fouls, goals, yellow cards, or red

cards – it also in log form because of the use of Poisson for count data; ζ is a constant; alpha is

the vector of away team fixed effects; eta is the vector of home team fixed effects; gamma is

the time trend to account for a linear progression of the specified outcome variable over the

seasons being analyzed; VAR is a dummy variable that equals one if the league during that

season is using VAR – this is the main variable of interest; and is an error term for any other

unobservables. To reiterate, the coefficient of important is 1.

Results

After running multiple regressions examining the effect of the introduction of VAR on

outcomes of soccer, the results were consolidated and can be seen in Table 1 and Table 2. Table

1 reports how the home team and away team were each affected by the introduction of VAR.

The home and away team were analyzed separately to determine whether this new technology

monitoring system affected teams’ playstyle. As evident in the table, only three of the

coefficients for VAR are significant. To interpret the significant coefficients, the values will need

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to be exponentiated because they are in log form.3 Now, it can be interpreted that when VAR

was used, home goals decreased by 10.4% compared to when VAR was not used with

significance at the 99.9% level. Home fouls were also negatively impacted with the introduction

of VAR by 3.9% compared to when VAR was not used; significance was again at the 99.9% level.

Lastly, away goals were negatively impacted by 8.4% compared to when VAR was not used with

significance at the 95% level. For all regressions, a vector of home team and away team fixed

effects and a yearly time trend were included to account for omitted variable bias and trends

across dependent variables.

Table 1

Table 2 represents the total effect of the introduction of VAR. This table was included to

demonstrate whether an innovative technology acts as a disruptor in an industry. The conditions

for interpreting the coefficients that were applied to Table 1 apply to this table as well. Only total

3 This can be done by using the following equation: (ex – 1) * 100; where x is the coefficient being analyzed.

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goals and total fouls were significant. When VAR was adopted, total goals decreased 9.5%

compared to when VAR was not used. Total fouls also decreased, by a lesser magnitude of 2.1%,

compared to when VAR was not used.

Table 2

Discussion & Conclusion

Unfortunately, most of the coefficients from the regressions were not significant. This

may be because of a type ll error, where there is an effect but not enough games and seasons

using VAR to have the confidence to show a causal relationship. For the five coefficients that

were significant, all of them indicated that VAR had a negative effect on those outcomes. The

number of home goals and fouls and away goals decreased after the introduction, in my

opinion, because teams played more hesitantly with this monitoring technology now being

used. Similarly, total goals and total fouls were negatively impacted, possibly because of the

reduced human error when the technology was utilized.

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Because of the lack of significant results, all conclusions should be taken with a grain of

salt. In general, the results show that technological advancements do disrupt the pertaining

industry, even if it is a small impact. It also shows that monitoring can affect employees; if the

monitoring has a positive or negative impact on the game is up for further discussion. Further

research needs to be done to make claims about how the introduction of a technology can

affect the pertaining industry.

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Appendix

Descriptive Statistics of Dependent Variables

Variable Obs Mean Std. Dev. Min Max

homegoals 10,273 1.544 1.308 0 10

awaygoals 10,273 1.183 1.165 0 9

homefouls 10,272 12.793 4.203 0 33

awayfouls 10,272 13.217 4.309 0 32

homeyellows 10,271 1.917 1.352 0 8

awayyellows 10,272 2.183 1.379 0 9

homereds 10,272 0.093 0.307 0 2

awayreds 10,272 0.120 0.345 0 3

totalgoals 10,273 2.726 1.676 0 12

totalfouls 10,272 26.010 6.641 5 56

totalyellows 10,271 4.100 2.137 0 13

totalreds 10,272 0.213 0.477 0 4

Source: football-data.co.uk

Descriptive Statistics of Bundesliga Dependent Variables

Variable Obs Mean Std. Dev. Min Max

homegoals 1,648 1.663 1.364 0 8

awaygoals 1,648 1.284 1.221 0 6

homefouls 1,648 13.048 4.282 2 29

awayfouls 1,648 13.757 4.483 2 30

homeyellows 1,648 1.654 1.247 0 8

awayyellows 1,648 1.975 1.272 0 7

homereds 1,648 0.072 0.269 0 2

awayreds 1,648 0.083 0.281 0 2

totalgoals 1,648 2.947 1.726 0 9

totalfouls 1,648 26.805 7.047 7 54

totalyellows 1,648 3.629 1.955 0 11

totalreds 1,648 0.155 0.408 0 3

Source: football-data.co.uk

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Descriptive Statistics of EPL Dependent Variables

Variable Obs Mean Std. Dev. Min Max

homegoals 2,188 1.529 1.283 0 8

awaygoals 2,188 1.185 1.168 0 9

homefouls 2,188 10.512 3.428 0 24

awayfouls 2,188 11.049 3.533 1 26

homeyellows 2,188 1.574 1.241 0 7

awayyellows 2,188 1.767 1.267 0 9

homereds 2,188 0.057 0.239 0 2

awayreds 2,188 0.077 0.271 0 2

totalgoals 2,188 2.714 1.634 0 9

totalfouls 2,188 21.561 5.117 5 39

totalyellows 2,188 3.340 1.923 0 12

totalreds 2,188 0.133 0.364 0 2

Source: football-data.co.uk

Descriptive Statistics of La Liga Dependent Variables

Variable Obs Mean Std. Dev. Min Max

homegoals 2,105 1.558 1.352 0 10

awaygoals 2,105 1.152 1.163 0 8

homefouls 2,105 13.783 4.247 1 33

awayfouls 2,105 13.827 4.187 0 30

homeyellows 2,105 2.460 1.536 0 8

awayyellows 2,105 2.716 1.486 0 9

homereds 2,105 0.105 0.325 0 2

awayreds 2,105 0.133 0.361 0 3

totalgoals 2,105 2.710 1.723 0 12

totalfouls 2,105 27.610 5.975 5 52

totalyellows 2,105 5.176 2.277 0 13

totalreds 2,105 0.238 0.495 0 3

Source: football-data.co.uk

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Descriptive Statistics of Ligue 1 Dependent Variables

Variable Obs Mean Std. Dev. Min Max

homegoals 2,176 1.480 1.280 0 9

awaygoals 2,176 1.094 1.128 0 9

homefouls 2,175 12.851 3.852 2 28

awayfouls 2,175 13.459 4.131 1 30

homeyellows 2,175 1.671 1.165 0 6

awayyellows 2,175 1.942 1.234 0 6

homereds 2,175 0.111 0.332 0 2

awayreds 2,175 0.142 0.372 0 2

totalgoals 2,176 2.574 1.617 0 9

totalfouls 2,175 26.310 6.053 10 50

totalyellows 2,175 3.613 1.848 0 11

totalreds 2,175 0.252 0.514 0 3

Source: football-data.co.uk

Descriptive Statistics of Serie A Dependent Variables

Variable Obs Mean Std. Dev. Min Max

homegoals 2,156 1.517 1.271 0 7

awaygoals 2,156 1.223 1.151 0 7

homefouls 2,156 13.889 4.265 3 29

awayfouls 2,156 14.167 4.451 1 32

homeyellows 2,155 2.185 1.302 0 6

awayyellows 2,156 2.485 1.370 0 8

homereds 2,156 0.116 0.345 0 2

awayreds 2,156 0.160 0.401 0 2

totalgoals 2,156 2.740 1.673 0 10

totalfouls 2,156 28.055 6.817 8 56

totalyellows 2,155 4.669 2.033 0 12

totalreds 2,156 0.276 0.551 0 4

Source: football-data.co.uk

Navigating football-data.co.uk:

• Once you enter the football-data.co.uk website, the data used can be accessed by clicking the “Historical Data” tab on the right panel. When that loads, you can scroll down and choose which league will be analyzed. After choosing the league, you will be brought to a page where you specify the year and specific division within the country of chose.

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References

• Pierce, Lamar; Daniel Snow and Andrew McAfee (2015) “Cleaning House: The Impact of Information Technology Monitoring on Employee Theft and Productivity,” Management Science, Vol. 61, pp. 2299-2319.

• Buchak, Greg; Gregor Matvos, Tomasz Piskorski and Amir Seru (2017) “Fintech, Regulatory Arbitrage, and the Rise of Shadow Banks,” Journal of Financial Economics, Vol. 130, pp. 453-483.

• Lago-Peñas, Carlos; Rey Ezequiel and Kalén Anton (2019) “How does Video Assistant Referee (VAR) modify the game in elite soccer?” International Journal of Performance Analysis in Sport, pp. 1-9.

• Bradbury, John C. (2018) “Monitoring and Employee Shirking: Evidence From MLB Umpires,” Journal of Sports Economics, Vol. 20, pp. 850-872.

• Mills, Brian M. (2017) “Technological Innovations in Monitoring and Evaluation: Evidence of Performance Impacts among Major League Baseball Umpires,” Labour Economics, Vol. 46, pp. 189-199.

• Hu, Yirui and Donald R Hoover (2018) “Non-randomized and randomized stepped-wedge designs using an orthogonalized least squares framework,” Statistical Methods in Medical Research; London, Vol. 27, pp. 1202-1218.

• Ford, M. (2018, February 17). Opinion: The decision is irrelevant; VAR sucks the emotion out of football. Retrieved April 26, 2020, from https://www.dw.com/en/opinion-the-decision-is-irrelevant-var-sucks-the-emotion-out-of-football/a-42631617

• VAR makes Premier League less enjoyable - YouGov survey. (2020, February 4). Retrieved April 26, 2020, from https://www.bbc.com/sport/football/51375410

• The top 10 Best supported leagues in the World. (2020, April 18). Retrieved April 25, 2020, from https://www.besoccer.com/new/https-betway-es-the-top-10-best-supported-leagues-in-the-world-645159

• The International Football Association Board. (2019). Video Assistant Referees (VARS) Used Live in Competitions and Leagues. Retrieved April 25, 2020, from https://www.theifab.com/projects/vars/background-scope

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Data Sources

• Football DataCo. (2001). football-data.co.uk. Date accessed: 2020-03-20.

• Matthias Seidel. (2000). Transfermarkt.us. Date accessed: 2020-03-20.