World Cup Preview: Who Will Win it All?

Authors: Manav Jain, Matthew Hobbs, Colin Wong, Alex Madden, Stefan D’Ippolito

Player Cards: Colin Wong

Editor: Manav Jain

The 2022 World Cup is here! Despite untraditional circumstances, with the tournament taking place in the middle of the season, the top teams are here to play - seeking to bring home the biggest trophy in all of sport. The QSAO team is here to break down the five biggest contenders in this tournament. According to an average of 16 data-driven models predicting the World Cup (assembled by Jan Van Harren), there is a 57.47% chance that one of Argentina, Brazil, England, France, or Spain will reign victorious. Let’s have a look at how the sides stack up.

Argentina

Getty Images

Championship Probability: 12.42%

Having a well-rounded team that just last year, won their first international trophy since 1993, Argentina are looking like legitimate contenders. Add on the important element that this will be the last World Cup for their captain and all time leading scorer, Lionel Messi, the Albicelestes should be more than up for making a deep run in the 2022 World Cup.

Tactical Overview

Argentina’s manager Lionel Scaloni took over the side after their Round of 16 exit in the 2018 World Cup. Since then, Argentina have only suffered defeat in four of the 50 games they have played, being unbeaten for over three years. 

Unlike Argentina’s sides of the past that have focused on fitting in as many of their talented attackers as possible, this team is focused on creating a strong and secure defensive foundation and running their attacks through Lionel Messi. Without the ball, Argentina tend to maintain a 4-4-2 formation, with their intense midfielders and attack picking moments to press the opposition while relieving Messi of such responsibilities. In their own half, Argentina are very compact and tough to break through, sitting in a 4-4-1-1 formation, and leaving Messi free for any counter attacking opportunities. 

Argentina’s Projected Starting XI and Key Substitutes

Argentina’s forward play is flexible, their attacking style is a mix of intricate passing buildups, as well as direct counter-attacks. Rather than being focused on a distinct method of attacking, Scaloni sets up his side to give his talented attackers the freedom of picking their opportunities to attack in different ways. Their formation in attack shifts to more of a 4-3-3, with the left sided midfielder tucking in and Ángel Di María pushing forward. Typically, the left back, Nicolás Tagliafico is given more license to get forward and overlap than his right sided equivalent.

Argentina’s build up play is variable, with the side willing to take both direct or measured approaches.

Liam Tharme/The Athletic

The Star

Since Scaloni has taken over, Argentina’s style has been focused on getting the best out of their star man, Lionel Messi. He enjoyed a fantastic 2021 Copa América campaign where he had 4 goals and 5 assists in 7 games. Lionel Messi’s strike partner for Argentina, whether it be Lautaro Martínez or Julián Álvarez will be intense defensively, picking up Messi’s defensive duties. In attack, Messi is at the centre of everything Argentina creates, and his performance in this World Cup will be a large amount of what decides Argentina’s fate. La Pulga is a complete attacking threat, ranking in the 99th percentile for dribbles completed, 98th percentile for non-penalty expected goals, and 97th percentile for expected assists [2].

The X-Factor

Giovani Lo Celso has been an important part of Scaloni’s Argentina side, playing a hybrid role between being a defensively responsible left midfielder and defense, while being able to move into midfield when Argentina have possession. However, a muscle tear and a subsequent surgery has resulted in Lo Celso being ruled out of the World Cup, leaving an important gap in Argentina’s midfield. Filling this hole will likely be Brighton’s Alexis Mac Allister who will be tasked with the responsibility of balancing his attacking and defensive responsibilities while playing a role he is not too familiar with. Mac Allister has enjoyed a fantastic start to the 2022-23 season with Brighton, albeit as more of a deep-lying playmaker. His ability to rise to the challenge in perform in this new role will be influential on how well Argentina will fare in this World Cup.

Brazil

Henry Romero/Reuters

Championship Probability: 19.52%

The tournament favourites, Brazil are in fine form ahead of their 2022 World Cup campaign, having gone unbeaten in the qualifiers for the tournament through 17 games, and conceding just 5 goals over that span. 

Tactical Overview 

Brazil played in several different formations in qualifying, but have looked their best playing a 4-2-3-1. They will tend to switch to a 4-2-4 when attacking, with Neymar shifting from a traditional number 10 role into a more advanced position alongside the other striker, who was most often Richarlison. They can easily transition from defence to attack through their double pivot of Casemiro and Fred, the former having been a key in numerous Champions League-winning Real Madrid sides. The fullbacks tend to stay quite narrow and don’t typically push on, whereas the wingers like to stay high and wide, often hugging the touchlines. These tactics allow Brazil to get the best from their star man Neymar who can drop deep to get a pass or drift wide and collect from one of the fullbacks. 

Brazil attempts to keep the ball in the midfield third, forcing the opposition to push up the pitch, after which they play balls for their attackers to run onto. 

As far as the Starting XI goes, most players seem nailed on, but both ST and LB seem to be up for debate. Tite has seemed to prefer playing Richarlison up top, however, there is a question of whether Jesus’ excellent form for Premier League leaders Arsenal be enough for him to get into the Starting XI. A key element Jesus brings over Richarlison is his playmaking which might be necessary to break teams down. Alex Sandro seems the more likely left-back to start. However, his recent form might leave a spot for Telles in the lineup. 

Brazil’s Projected Starting XI and Key Substitutes

The Star

Although it might be the most obvious pick, Neymar is certainly Brazil’s star player, and their success will rely primarily on getting the best out of him. His 8 goals and 8 assists in 10 matches led Brazil in both categories as he looks to continue that form into what he said could be his last World Cup. He ranks in the 99th percentile for Shot-Creating Actions per 90 (6.59) and Progressive Passes (6.59) per 90 showing his influence in Brazil’s attacking efforts as he often is involved in bringing the ball toward the goal or creating a shot opportunity for his teammates as well as scoring numerous himself.

The X-Factor

A problem Brazil could find themselves in is needing something past the hour mark in a match, considering it would be expected that most teams will sit back against them. Thus, a fresh set of legs will be very important and as far as super subs go, Rodrygo finds himself right at the top of that list. Although the 21-year-old has not had many chances to play for Brazil in the qualifiers, his form at Real Madrid this season might be enough for him to make appearances off the bench for Brazil. His famous double against Manchester City in the latter stages of the 2nd leg of the Champions League semi-final still comes to mind as one of the most clutch performances in recent history. The right-winger ranks in the 96th percentile for Touches in the opponent’s penalty area per 90 with 6.72 as well as the 93rd percentile for pass completion % per 90 with 83.9%. The winger also averages over 3 shots per 90, meaning his intent towards goal could be vital in getting Brazil a late goal.

England

John Walton/PA Images/Imago Images

Championship Probability: 6.98%

England’s recent international form is a topic of great discussion approaching the world's biggest sporting event. With two recent losses to Hungary, one to Italy and relegation in the Nations League Gareth Southgate’s job relies on a strong result in this World Cup.

Tactical Overview

Gareth Southgate’s brand of football is not very popular amongst fans. Many have said that England’s style of play is very boring and defensive. It is no secret that England plays a style of football that allows the opposing team to play onto them. While maintaining the pace of play, the side most often finds themselves in their own half, building up slowly and meticulously. With this in mind, England is forced to solely play on the counter and attack from set pieces. They ranked 1st in UEFA for set piece goals and 2nd in set piece xG during the World Cup Qualifiers. Another fair criticism heading Southgate’s way is their habit of parking the bus after taking leads in big games. Such was the case in the Euro 2020 Final, World Cup 2018 Semi-final, and the quarter-final in which they all failed to hold onto 1-0 leads.

Formation wise this World Cup, we will likely be seeing England play in a 4-3-3 formation with one defensive midfielder and two central midfielders. England can play very dynamically as sometimes they transition into a 4-2-3-1 with either Mount or Grealish taking the role of the number ten. During the European Championship, they also played a hybrid of 3-4-3 where Kyle Walker slipped into a centre-back role and Kieran Tripper into a right-wingback role. This will likely be the case in certain games later in the tournament where England faces higher-quality opposition. England can be very creative in attacking midfield as they have many players to fill the roles such as Phil Foden, Jack Grealish, and Mason Mount. All three can play on the wing as well.

England’s Projected Starting XI and Key Substitutes

The Star

The only place where England is non-negotiable is up front where Captain Harry Kane has featured in most games for the squad in the last 5 years. For both his goal-scoring ability and his creativity, Harry Kane is the man to lead England into Qatar. Second in all-time scoring for England at 51 goals, Kane’s finishing has been elite for years. He is also an incredible asset in the air, which plays to England’s liking of set pieces. Kane, scoring 16 of 19 senior England penalties is also an elite penalty taker, almost guaranteeing conversion for England in key moments.

The X-Factor

With a burst of recent form from Arsenal’s wonder boy, Bukayo Saka is heading in the right direction to make a big impact for England in Qatar. Saka is a very busy attacker who gets tons of touches in the attacking third and will hopefully pair up well with Sterling and Kane as he does with Gabriel Jesus and Martinelli back in London. He is in the top 25% for all expected goals, assists, non-penalty goals, and total shots. His killer instinct in front of goal this year has been a key to Arsenal’s recent success as well as his ability to create big chances. Saka continuing his club form onto the international level will be crucial for England’s World Cup chances. 

France

James Hill/New York Times

Championship Probability: 9.41%

As the reigning World Cup winners, France will be looking to become the first nation since Brazil (1958, 1962) to win back-to-back World Cup titles. Les Bleus will also be looking to end a World Cup champions’ curse dating back to 1998. Since 1998 there have been four World Cup winners from Europe (France 1998, Italy 2006, Spain 2010, and Germany 2014), all of whom have failed to progress from the group stage of the following tournament. 

Tactical Overview

Injuries to key players Pogba, Kante, and Benzema have created chances for several less-established players like Tchouameni and Rabiot to step into the starting XI.  

Benzema’s injury, in particular, will change the way that France will look to attack. Instead of Coach Didier Deschamps’ preferred 4-3-1-2 formation with fluid movement between the front three of Benzema, Mbappe, and Griezmann up top, he will likely shift to a 4-2-3-1 formation with Giroud leading the line and the trio of Mbappe, Griezmann, and Dembele behind him in support. Alternatively, Deschamps could lineup more defensively in a 4-3-3 shape, replacing one of his front four (likely Giroud or Dembele) with the more defensive-minded Camavinga or Guendouzi.

Statistically, Giroud and Benzema profile as very different players, highlighted by Giroud's lack of involvement in build-up play as seen by his 22.15 passes attempted per 90 minutes (47th percentile), 0.84 progressive passes (16th percentile) and 0.2 dribbles completed (2nd percentile). Meanwhile, Benzema averages 44.61 passes attempted per 90 minutes (98th percentile), 3.78 progressive passes (96th percentile), and 0.89 dribbles completed (54th percentile).

Giroud likes to play as more of a target man, looking to get involved around the box often with his back to goal with balls played to feet or in the air, with the trio of players behind him looking to run into the space past him or to receive the ball in nearby positions with a short layoff. France will not rely on Giroud to score goals; in fact, he failed to score a goal or to even register a shot on target in the 2018 World Cup, the burden will instead be placed on the combination of Mbappe, Griezmann and Dembele.

France’s Projected Starting XI and Key Substitutes

The Star

With the Ballon D’or winning Benzema having to pull out of the French squad due to injury, France will need Mbappe to step up and score goals to go deep in the tournament. Mbappe comes into this World Cup looking to build on his last, where he scored 4 goals as a teenager and was awarded the Best Young Player Award. The now 23-year-old Frenchman has netted 12 goals in 14 Ligue 1 appearances this season for his club side PSG, ranking him in the 99th percentile of forwards in Europe’s Big 5 leagues for goals per 90 minutes, and has additionally ranked in the 99th percentile for total shots, non-penalty expected goals + assists, touches in the opposition box, and progressive passes received. The average of 11.76 progressive passes received per 90 minutes is a good representation of how Mbappe likes to play. He will be expected to exploit the spaces in behind opposition defences with frequent runs beyond the striker Giroud.

The X-Factor

With injuries to established midfielders Pogba and Kante, France will be relying on Tchouameni to run the show in the midfield at this World Cup (and for many future tournaments). Having been signed for a reported €80 million fee last summer by Real Madrid (ESPN), expectations are high for the deep-lying midfielder. Tchouameni excels defensively both on the ground and in the air. According to FBREF, Tchouameni averages 2.72 interceptions per 90 minutes (99th percentile) and wins 2.19 aerial duels per 90 minutes (91 percentile). Tchouameni is also comfortable in possession and build-up play. The midfielder averages 5.61 progressive passes per 90 minutes (93rd percentile), completes an average of 68.4 passes per 90 minutes (91st percentile) and completes his passes at a rate of 88.3% (89th percentile). For France to go far in the tournament, a big performance from Tchouameni will be required. Expect Tchouameni to show why Real Madrid paid an initial fee of €80 million last summer to sign him from Monaco CF and to cement a place in the French starting XI moving forward.

Spain

Reuters

Championship Probability: 9.14%

Spain has been a powerhouse in international football for many years with the heights of their power taking place from 2008-2012 when they won two Euros and one World Cup in succession. With the likes of Xavi, Iniesta, Casillas and many more in their prime, one can see why they were so dominant. Now with a fresh group of young and hungry Spaniards, the question remains, can they reach the heights that the legends of before achieved? With their strong showings in Euro 2020 and World Cup qualifiers one would be mistaken to think otherwise. 

Tactical Overview

With the guidance of their manager, Luis Enrique, this young Spanish team encapsulates Spanish football to the fullest. They play high-possession football with their last time registering under 60% possession being in March 2020. Not only do they keep possession well, but they are one of the best-pressing teams in the tournament. In Euro 2020, Spain had a press success rate of 36.4%, rated first in the tournament. They also had the best Sequence Start Distance in the tournament at 47.3 meters, meaning they were winning the ball higher up the pitch than all the other teams. Spain tends to play in a 4-3-3 formation with one holding midfielder and two more creative outlets on either side of him. The four defenders usually play a high line to keep up with the press, with the fullback overlapping on occasion. With the fullbacks overlapping, the wingers tend to cut on the inside to allow space for them. 

Spain’s Projected Starting XI and Key Substitutes

The Star

Despite only being 19 years old, Pedri has already established himself as a certified starter in Luis Enrique’s team. Pedri is essential in linking up the defence and the attack and he is in good form for Barcelona, where he plays his club football. Pedri is also an extremely effective transition player, averaging 5.42 progressive passes and receiving 4.57 progressive passes per 90 minutes played, which are in the 90th and 95th percentile respectively, of players in Europe’s top 5 leagues. Given Spain’s possession style of play, their midfield is of utmost importance in creating chances and organizing the press. This means Pedri will be tested on countless occasions but given what he has shown in his young career, there is every reason to believe he will continue to shine as Spain’s star player. 

The X-Factor

Even though Spain does an excellent job at keeping possession, they do not tend to put the ball in the back of the net as often as most would think. In World Cup qualifying, Spain averaged 1.87 goals per game. This does not seem awful, but when comparing Spain to the teams they were playing, such as when they put 4 goals past the 78th-ranked Georgia, this is not up to Spain’s standards. Also, when comparing them to other top nations, such as Germany, who averaged 3.6 goals per game and England, who averaged 3.9 goals per game, one can see the problem Spain has. Morata is key in fixing said problem and just might be the key to Spain winning the World Cup. Morata, like Spain, did not have a fantastic World Cup qualifying in terms of scoring. He averaged 0.37 goals per 90 minutes played. With that being said, Morata has enjoyed good club form this year averaging 0.53 goals per 90 in a very defensive Atletico Madrid side. If Morata can keep this form going, or even improve, then Spain has a very good chance at going all the way in this year’s World Cup. 

With so many talented players and teams at the World Cup, results could go a number of ways. Sides that we have not even covered such as the Netherlands, Germany, Belgium, Portugal, Uruguay, and more could all conceivably win the World Cup as well, it is tournament football after all. Let us know which side you think will triumph in 2022 on social media or the comment section below!

Data Sources: Jan Van Haaren, FBREF, The Athletic, ESPN, Tifo IRL, Transfermarkt

An xGuide to Soccer Analytics

By Anthony Turgelis (@Anthony Turgelis) and Erik Kiudorf

The State of Soccer Analytics

Relative to other major sports, soccer lags behind with regards to its acceptance of analytics within the game. Soccer is an extremely traditional sport that is usually reluctant to change, so this should not come as a huge surprise. While there are some that are ignoring, there are some that are using this as a competitive advantage - and it’s really working in some cases.

In a game as fluid as soccer, it is difficult to understand the game objectively amidst differing opinions from players, fans, coaching staff and the media alike. However, the recent growth of analytics in soccer provides an element of objectivity. It introduces new measures of predictability that encourage analysis, in an area where it is currently lacking.

Another reason that soccer analytics lags behind to the public eye, is due to the rarity and inaccessibility of the data. Not to mention the complexity and quantity of data required to fully capture value on an open-play sport with infinite game outcomes. The company that holds the monopoly on advanced soccer data is called Opta, and they track every game in every major soccer league around the world. Since there are a lot of games to cover worldwide, lots of things to track, and only a few groups doing it, it’s not hard to see why this data is easy to monopolize. As a result, this data is either difficult to scrape from the web, or too expensive for personal use as it is believed to be priced in the four digit range per year for a license for a single league’s worth of data, but obviously this varies by use and is not confirmed by Opta themselves. As a result, it is difficult, but not impossible, to practice public soccer data analysis.

There are still other ways though! Sites like WhoScored and Squawka offer simple game stats for teams and players, although they are not exportable with traditional methods. For MLS specifically, American Soccer Analysis offers many features to get your fix for advanced stats, which will be highlighted throughout the article. These concepts can be used as evaluation tools, to confirm the eye-test, or to just enhance the viewing experience of the game.

How Teams are Using Analytics

Although statistical analysis is not new to soccer - where pass counts, pass completions and shots taken, for example, are often recorded - such stats only provide information of certain events in the game, while lacking further insight. Soccer analytics helps identify and acquire insight regarding potential players’ performances based on previous data sources collected from past performances. These advancements enable coaches and managers to utilize this data to plan more effective training programs, team selections, and game strategies.  

Analytics can be broken down into technical and physical categories. The physical aspects account for distance covered, intensity, number of accelerations and decelerations and jumps and lands. This data is most often utilized to monitor individual training loads which helps minimize injuries. The Seattle Sounders of Major League Soccer mainly focus on sports science along with physical analytics to ensure players are at their physical peaks and to prevent injuries

However, technical analytics act as a tool to help players and coaches to quantitatively assess individual and based team performances. This information is used to improve both individual and team performances and design successful strategies for upcoming games. These mechanisms can also provide knowledge to predict outcomes of games, create new game strategies, determine the price value of a player and connect players to brands and sponsorship opportunities. Devin Pleuler, Senior Manager of Analytics at Toronto Football Club, explains the importance of analytics in Major League Soccer “The players are on a salary cap but the analytics department is not so it’s a way you can set yourselves apart in a relatively cheap manner”. Analytics helps us quantify individual in-game events to provide an understanding of the probability of success, often evaluated by estimating goal scoring potential. It assigns values to the events - events being each stat category - to help better understand and coordinate tactics and systems. Coaches and managers can use this data to tailor tactical systems for upcoming games that are backed by objective information, translating to higher success rates on the field.

It's no surprise then, that in a game where analytics is finally starting to carve out a place for itself, that the two using it the most heavily in the MLS, have ended up in back-to-back MLS Cup finals against each other. Fun tidbit, when these two teams first competed in the MLS Cup finals, TFC's Senior Manager of Analytics challenged the Sounders' Director of Analytics, Ravi Ramineni, to a friendly wager:

Hey @analyseFooty, I'd like to propose a friendly #MLSCup wager:

Winner keeps the loser's calculator. #nerdderby pic.twitter.com/X0Wl1KqJO7

— Devin Pleuler (@devinpleuler) December 5, 2016

No word on whether Devin actually gave up his calculator or not, as TFC did end up losing that round. If he did, perhaps he got it back the next year when TFC was victorious over the Sounders.

Expected Goals (xG)

The most popular and most cited advanced metric in soccer analytics is Expected Goals (xG). Generally, expected goals is the count of how many goals a player should have been expected to score on, based on the quality of their chances. There are many models attempting to capture this, some better than others, but none are perfect. The main two inputs that can be found in most, if not all xG models, is where the shot took place, and how the shot was taken.

The ‘where’ of the shot refers to both the distance and angle of the shot. Logically, it seems to make sense that the further away a player is from goal the less likely their shot is to result in a goal. This becomes reflected in this statistic as shots from distance generally have a lower xG than close ones. In American Soccer Analysis’s model, they consider how much of the goal mouth is available to shoot at. The closer a player is to the goal line the less goal mouth will be directly exposed to him. To compensate for that a sharper angle will result in a decrease in xG.

Determining how the shot was taken is a slightly more complicated, as it is composed of the manner in which the physical shot is taken, as well as the lead up play to the shot. Higher probabilities are awarded to shots taken with the player’s foot rather than the head. This is simply because statistically a shot taken with the foot is more likely to score than a header. The build up play before the shot will affect the xG rating. For example, a shot taken from 10 yards on a counter attack will be awarded a higher xG then the exact same shot resulting from a corner. The reason for this is a concept is due to the time and space that the player would be allowed. Typically, on a fast break a player has more space and is able to get off his preferred shot. Whereas with a corner, the eighteen-yard box is very clogged so players are rushed to shoot and the chance of the ball being deflected is much higher.

What Can xG Tell Us?

Reasonable conclusions that can be drawn from xG are how often a player is in a good spot to score, and makes themselves available for good chances. Comparing their expected goals to their actual goals will give you an indicator of a player’s finishing ability, and whether they’ve benefited from good or bad luck. Think of it this way, if a player misses a sitter in front of the net by skying it over the bar, this type of shot from that location could be expected at (making this up) 95%. This player’s goal count would be zero, but xG count would be 0.95. The player got into a good position to score, but performed weakly in finishing. If they kept this up, there would be a large gap and this player could be deemed a poor finisher.

On the other hand though, let’s say two players in two different games take the same shot (which is deemed to be a 50% shot, or a 0.5 xG) against two goalies that are standing in the same spot. One goalie dives across and makes an incredible save, while the other falls just short. The player who did not score is penalized in goals for unluckily going up against a better goalie, which is out of their control. Sometimes, factors that are out of player’s control can affect their xG count in the short-term, while normalizing closer to the real goal total in a larger sample where luck would not affect them as much.

On AmericanSoccerAnalysis.com, you can find constantly updated MLS xG counts by game, player, and team. On Twitter, @11tegen11 tweets out a game maps of xG that were accumulated by each team in the game, and gives the odds of each team winning based on their xG count. This is a great way to identify which teams really got the better chances, but ran into some bad luck or good goaltending. His charts typically look like this:

Each scoring chance is denoted by the bar moving higher. The larger the rise of the bar, the higher the xG of the scoring chance, which means the more likely they are to score. In this came, it can be seen that Jelsson Vargas scored on a ~0.1xG chance, meaning he would be expected to score on that chance once every ten tries. The final xG coutns were 1.27 for Montreal, and 0.96 for Toronto, leading to the conclusion that it was a fairly even game that could have gone either way. This can also be seen in the match odds near the top left (that looks like a France flag for this game). What these mean are that in games where one team put up ~1.27 xG, and the other put up ~0.96, the team with the higher xG would be expected to win 43% of the time, draw 30% of the time, and win 28% of the time. TFC can consider themselves slightly unlucky to come out of this game without a point.

Expected Assists (xA) and Key Passes

xG is the most common tool to analyze how dangerous an attacker is. However, it doesn’t take into account how effective a passer is. That is why the stat ‘expected assists’ or xA was created. Expected assists is designed to give credit to the player that creates a chance not just the player who takes the chance. The way it does this is by assigning the xG rating of the chance to the passer in the form of xA. Therefore, if a through ball leads to a chance with an xG rating of 0.4 the player who laid the pass would be assigned an xA rating of 0.4.

Adding on to the playmaking measurement is key passes. Key passes are defined as “the final pass or pass-cum-shot leading leading to the recipient of the ball shooting”. The beauty of this stat comes from its simplicity. As long as the receiving player shoots the ball the passer is awarded a key pass regardless of the result of the shot. Therefore, it is quite easy to track and look out for during a game and will give the viewer a decent sense of which players create chances. However, the simplicity of key passes are also their downfall. Because every key pass is awarded the same rating of 1 it does not account for the type of chance created. A three-yard pass leading to a shot that goes ten yards wide is worth the same amount as a through ball leading to a tap in. Unlike xA, key passes do not differentiate and are less effective at actually measuring the total effect of creativity of a passer.

Player Comparison (Radars)

One the most useful, and easy to interpret tools (mostly) available to the public community are player radars. Due to the data constraints outlined earlier, it’s not so easy for everyone to make them, but there are thankfully a few people on Twitter who post them on a consistent basis, and that has essentially created a database of them on there. Here’s an example of a player radar created by Ted Knutson (@mixedknuts), for Sebastian Giovinco in  the 2016 season:

It might look like there’s a lot going on there, but it’s actually quite simple. Eleven stats are highlighted above, chosen by their position (in this case, forward). Each are presented in a per90 basis, so everyone is judged by the same scale. The closer each value stat is to the outer areas of the circle, is the closer that this player was to being the best in their respective league at it. The outer circle represents the top-5 percentile, while the middle of the circle represents the bottom-5 percentile for players in the same competition. If a player has a stat that touches the end, they are likely to be considered elite in that category. If they have a stat near the middle, this might be an indicator of their play style or they may have work to do. 0.39 throughballs has no relation to 1.2 dispossessions at all, aside from representing the same percentile rank for each different stat.

From this radar, we can see that Giovinco is an extremely high volume shooter, which is reflected in his high shots per 90, and low xG per shot. At first glance, his passing % looks weak, but considering that his passes into box number his well above average, he could be thought of as a creator near the goal. You probably already knew this, but the radar makes significant claims that Sebastian Giovinco is a fantastic soccer player, and has dominated the MLS. This really highlights the beauty of soccer analytics - it’s a great way to confirm the eye-test.

To access these player radars, it’s not an ideal process. First, go to The Twitter Search Page (does not require an account). The three people who have been identified that consistently post these are: @Mixedknuts, @Fussballradars, and @thefutebolist. Type any of their names (start with @Mixedknuts, his database is probably the largest, then move on to the other two) and then the name of the player you are looking for. It’s sometimes best to then filter by photos, as all the radars will appear there. You could then have found the radar you are looking for. If that didn’t produce any results, it’s not entirely hopeless. Ted Knutson occasionally opens a request line on Twitter, so if you want a radar for a player who does not have one yet, you can request one that way.

Score Effects

Score Effects are an important concept to consider, especially for casual viewing, as it might help explain certain phenomena that occur every single match. The idea here is that when teams are winning, they tend to sit back and defend more, and while they are losing, they push forward. Seems obvious, right? The thing that is not always obvious to most people is how this will affect the flow of the game, the final stat-line, and the quality of shots that can be expected. Statsbomb did a detailed statistical analysis on score effects which can be found here, which shows some of the math and stats they used to confirm this effect.

Essentially, what they found was that when teams were leading in a game, they tend to form a ‘defensive shell’ which will tighten them up defensively, and drop deeper. This is done because to them, preventing a goal would be more valuable than scoring another. They tend to allow more shots from a further distance out, and these shots typically are less likely to go in.

On the other hand, when teams are trailing by a goal, they will tend to take more shots in a more desperate attempt to score the tying goal. These shots will typically be of lesser quality due to this desperation and by not being afforded the freedom to wait for the perfect chance to become available. The conversion rates on these shots tend to be lower, which is another hat-nod to the notion that these shots are of lesser quality.

Add all of this up, and you could see a very lopsided statline at the end of the game if one team happened to be trailing for the most of it. It might paint a picture that one team dominated and got lucky. This could be true, but hopefully with knowledge of the concept of score effects, you will be able to see through this scoreline and consider that these shots could have been lower quality and part of the defending team’s plan all along.

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Cover photo credited to Reuters