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Following our Nearest & Dearest Rivals 2019/20

Itsintheblood89
in Football (Other Teams)

Featured Replies

https://www.bbc.co.uk/news/uk-wales-51870306

The pilot of the plane that crashed killing footballer Emiliano Sala was not licensed to fly the aircraft, a report has found.

Sala, 28, and pilot David Ibbotson died in the crash, two days after the Argentine striker signed for Cardiff City in January 2019.

The Air Accidents Investigation Branch published its findings on Friday.

It said Sala would have been "deeply unconscious" from carbon monoxide poisoning at the time.

https://www.bbc.co.uk/sport/football/51895453

Jan Vertonghen's family was held at knifepoint as four armed men burgled his home while he was on Champions League duty with Tottenham on Tuesday.

Police confirmed men wearing balaclavas forced their way into the house while his wife and children were inside.

The 32-year-old Belgium defender was in Germany for the second leg of Spurs' last-16 defeat against RB Leipzig.

"We have been supporting Jan and his family through this terribly traumatic time," said a Spurs spokesperson.

"We encourage anyone who has any information to come forward to help the police with their investigation."

A Met Police spokesman said: "Police were called to a residential address in NW3 at 7.49pm on 10 March to a report of a burglary.

"Officers attended. It was reported four men wearing balaclavas, armed with knives, had forced entry to the property and stolen a number of items before leaving.

"Nobody was injured. The suspects had left the scene before officers arrived. There have been no arrests and inquiries continue."

Vertonghen is the latest footballer to be targeted by criminals.

In December burglars broke into the London home of Crystal Palace defender, Mamadou Sakho, reportedly stealing valuables worth more than £500,000.

Arsenal footballers Mesut Ozil and Sead Kolasinac were attacked by carjackers in July 2019, while Liverpool forward Sadio Mane had his house burgled while he was playing in a Champions League last-16 tie against Bayern Munich in February 2019.

On 13/03/2020 at 00:55, dkw said:

If the league gets cancelled now and Liverpool aren't crowned as trfue champions I'm not sure I will ever stop laughing.... Ever. 

I wholeheartedly agree!

https://www.bbc.co.uk/sport/football/51901183

The Italian Football Federation has called for the postponement of Euro 2020 to give time to complete the Serie A season, which has been suspended because of coronavirus.

Federation president Gabriele Gravina says the idea will be put to Uefa at an emergency meeting on Tuesday.

Serie A was suspended last Tuesday, with Italy the European country worst affected by the virus.

"We will propose that Uefa postpone the European Championship," Gravina said.

Speaking to television channel Sportmediaset, the Italian added: "We will try to get to the end of this championship [Serie A] because it is fairer and more correct after the many investments and sacrifices of our clubs."

Uefa has invited representatives of its 55 member associations to Tuesday's video conference meeting, with Euro 2020 due to take place from 12 June-12 July at venues across Europe.

The boards of the European Club Association and the European Leagues, and a representative of world players' union Fifpro have also been invited and discussions will cover all domestic and European competitions, including the possible postponement of Euro 2020 by one year.

The Italian Football Federation had previously said the Serie A season may not be completed and offered alternatives including play-offs, not having a champion for 2019-20 or declaring the current standings final.

Gravina now hopes the league could finish by 30 June, one month later than scheduled, but admitted it could be extended for another month and suggested clubs should not train at the moment.

"If the league were to resume at the beginning of May, I would leave the question of training for the moment," he said.

"Let's leave the boys at home, they have to recover physical and mental energy."

On Sunday, Italy reported 368 more coronavirus deaths, a new one-day record in the country.

I was reading my Dad's Mail yesterday and had a photo of Son training at White Hart Lane and talking with Mourinho, interestingly I could not find this on-line at the Mail. What interested me what the last time I had heard about Son was he was being held in Korea re the virus outbreak.

2 hours ago, Strider6003 said:

I was reading my Dad's Mail yesterday and had a photo of Son training at White Hart Lane and talking with Mourinho, interestingly I could not find this on-line at the Mail. What interested me what the last time I had heard about Son was he was being held in Korea re the virus outbreak.

Could it be an old picture?

The 10 Commandments of football analytics

https://theathletic.com/1692489/2020/03/23/the-10-commandments-of-football-analytics/

 

the-10-commandments-stats-analysis-scale

 

Last year, The Athletic’s Ben Baldwin wrote a piece detailing the 10 Commandments of numbers-based analysis of the other football. The one with the funny-shaped ball. The beautiful game lends itself to plenty of analysis using numbers, too but just because the data is there, it doesn’t mean that it’s always used correctly.

This guide will give you a better appreciation of the context required when talking about teams and players, which numbers to focus on and how to better question what you’re seeing.

Here are The 10 Commandments.

1) Thou shalt not use save percentage to evaluate a goalkeeper’s shot-stopping ability

Example: “Martin Dubravka has been the eighth best shot-stopper in the Premier League this season with a save percentage of 73.9 per cent”

Why it’s misleading: The equation for save percentage is shots saved/total shots faced. Straight away, there’s no accounting for the difference in the type and quality of shots that a goalkeeper faces, which will have a large impact on his ability to make a save, and therefore, his save percentage.

Goalkeeper X facing 10 shots from inside the six-yard box is going to have a tougher time making saves compared to Goalkeeper Y, who’s facing all of his ten shots from 30 yards out or more.

Expected Goals and its cousin, Expected Goals on Target, tell us that shots from further away are less likely to result in a goal and shots that are either right at the keeper or down the middle are more likely to be saved. Anyone reading who has watched enough football will, of course, tell you the same thing.

By equally weighting each shot to calculate save percentage, we are doing a disservice to Goalkeeper X and making Goalkeeper Y look better than they actually might be.

What to use instead: Comparing the quality of on-target shots by using Expected Goals on Target (or Post-Shot Expected Goals) to the number of goals conceded, which I’ve written about previously here, adds much needed context to a goalkeeper’s numbers.

Goals Prevented tells us how many goals a goalkeeper saved given the quality of shots he’s faced, compared to the average goalkeeper. Through doing this, Martin Dubravka looks far better than his save percentage says he is, and Vicente Guaita looks like a world-beater:

 

gk_gp_save_table-1-679x1024.png

2) Thou shalt not use distance or sprint stats to indicate effort

Example: “Mesut Ozil has run more than any other player for Arsenal today, clocking up 11.2km”

Why it’s misleading: Premier League clubs have had access to tracking data since 2013-14 and, as part of that deal, the media get access to derived outputs too. Up to this point, all we’ve really seen is distance and speed statistics.

The reality is, these numbers are some of the most contextless around, yet they’re used frequently when analysing teams and players. The reasons for not using are plentiful.

Firstly, there’s no correlation between the distance you run and your likelihood of winning a game. The amount of distance covered in a finite amount of time is only useful in a time trial, which football is not. From last year’s UEFA Technical Report on the Champions League, Shakhtar Donetsk ran the furthest on average of all 32 teams in the competition, yet finished third in their group and crashed out of the Europa League in the round of 32. Manchester United ran the second-least on average, yet were still able to reach the quarter-finals. Distance doesn’t really tell us much.

Secondly, distance and sprints are going to be stylistic, as in, the numbers that players rack up will be linked to what’s asked of them, the system they play in, how the opposition sets up, game state, and various other factors. Without controlling for — or at least mentioning — these other factors, these numbers don’t give us much insight.

Finally, there’s also some evidence to suggest that running less actually can be beneficial — just ask Lionel Messi. Most players have the fitness levels to last a full game but the manipulation of space is what matters. Similarly, there have been plenty of quick players to have played the sport but the very best know when to use their pace. Very rarely do players need to beat another in a foot race but it’s quick bursts of speed to get past someone or latch on to the end of a loose ball that are key.

There’s value in this data but it’s on the athlete-management side and ensuring that the players are in the right condition to be playing. Football is a game of space and time, and the current tools to measure these are too blunt to be interesting right now.

What to use instead: There’s not really a great substitute here. Either these numbers need to be framed properly before using or we’re probably better off without them.

3) Thou shalt not use possession as an indicator of quality

Example: “Tottenham had 79.8 per cent possession in their 0-1 defeat to Newcastle; the second-highest figure for a losing side in the Premier League since 2003-04.”

Why it’s misleading: As Marti Perarnau puts it in Pep Confidential (my pick for The Athletic’s list of favourite football books) “possession is only a means to an end. It’s a tool, not an objective or an end goal.” Leicester City won the league averaging 42.6 per cent of possession in 2015-16. Manchester City won the league last season averaging 67.7 per cent of the ball. In essence, it doesn’t matter how much you have — it’s what you do with it.

Winning the possession battle doesn’t really tell us that much beyond how teams stylistically set up to play and in-game, can be entirely dictated by the scoreline. Take Atletico Madrid’s 1-0 victory recently against Liverpool in the Champions League. After a fourth-minute goal, Atleti set up shop, having just 27 per cent of possession. That figure may have looked entirely different had Atleti not scored early on.

What to use instead: Possession is still a useful nugget of information to understand which side had more of the ball — but just don’t use it to win any arguments that one team is better than another. Expected Goals is a far better indicator of the quality of a team, so if you want to argue about quality, see how good your team is at creating and preventing goalscoring chances.

4) Thou shalt not judge a player’s defensive ability on the number of tackles and interceptions they make

Example: “Ricardo Pereira is the best defender in the Premier League, making 119 tackles this season”

Why it’s misleading: Not all the defending that a player does is tangible and the measurable output that can be counted is often biased by team style. Logically, if a team has less possession, they have more opportunities to defend, and vice versa.

For that reason, tackle and interception numbers are better indicators of defensive style (i.e. is the player passive or active) and not necessarily the defensive quality of a player. Virgil van Dijk attempts just 0.76 tackles per 90 minutes, yet no one would make the case that that makes him a poor defender.

In addition, because these defensive numbers are at the mercy of the style of team that a player plays in (mainly the frequency of time they are out of possession and therefore are called into action), it’s hard to compare one player to another.

What to use instead: To combat this, we can adjust defensive statistics for the number of times that they make these actions for every 1,000 touches that an opponent makes when on the field of play — an interpretable method of getting all players on a level playing field. Jordan Henderson’s 2.6 tackles per 90 is 15th best in the league but, when adjusting for possession, he jumps to 4.6 per 1,000 opponent touches, the fifth most defensively-active midfielder in the league.

Possession-adjusted defensive numbers give a more rounded view of defensive activity but these still only show style and not overall quality.

5) Thou shalt not use tackle win-rate to judge a player’s tackling ability

Why it’s misleading: I’m going to let you into a secret: tackles lost and tackles won are practically the same thing and ignore two other key outcomes when trying to make a tackle.

Tackles are usually split into two categories — those that are won and those that are lost. Winning a tackle consists of a player winning back possession when challenging for the ball, while losing a tackle sees a challenge take place but the ball isn’t won back. Losing a tackle could be due to the ball being poked out for a throw-in for the opposition, the ball knocked loose for the opposition to recover, or some other reason.

Tackle win-rate is currently defined as tackles won/(tackles won + tackles lost). What this currently tells us is the proportion of tackles that a player makes where his team wins the ball back.

What’s the problem? Well, this currently ignores times when a player attempts a tackle and gets bounced off the player currently in possession, or when attempting a tackle, commits a foul. Of full-backs in the Premier League with the highest tackle win-rate, Martin Kelly is the best with 80 per cent of tackles won. The eye test tells us Aaron Wan-Bissaka should be amongst the top players, yet he’s only 11th. What gives?

What to use instead: True tackle win-rate can help avoid this error by incorporating these two missing categories, with the equation of total tackles/(total tackles+challenges lost+fouls when attempting a tackle). Through this metric, Wan-Bissaka is top with a 78.9 per cent true tackle win rate, and Martin Kelly is down in 29th — much better.

 

true_tackle_win_rate_table-1.png

6) Thou shalt not use goals minus expected goals as an indication of finishing ability in small samples

Example: “Roberto Firmino has only scored eight goals from 12.7 xG, therefore he’s a poor finisher.”

Why it’s misleading: When it comes to understanding goalscoring ability, there are two crucial elements that need to be considered and judged in isolation. The first is a striker’s ability to generate chances for himself. Goals are a striker’s main currency and to score goals, strikers need to take shots. To measure the quality of these shots, we use expected goals. If a player consistently gets into good goalscoring positions, over time, goals will come.

It’s one thing taking shots, it’s another thing to finish them. In small samples — such as a whole season — a player’s goals and xG may not match up. Take Roberto Firmino. This season, he’s scored fewer than you’d expect given the chances he has but it is his best in terms of getting into great goalscoring positions.

firmino_xg_table-1024x510.png

Firmino’s three prior seasons at Liverpool have seen him score above, below and on expectation. This isn’t enough data to give any concrete conclusions on his finishing ability.

What to use instead: Comparing expected goals (the chances players have) with expected goals on target (what they do with those chances) is one method of considering finishing quality in a very basic way. Even over larger samples, use with caution, and consider at least several hundred shots.

There’s a lot of debate in football analytics circles of whether finishing is a repeatable skill, though, so until there’s a proper answer, go ahead and rely on expected goals’ indication that, over time, most players score in line with their xG.

7) Thou shalt not judge a team’s performance with or without a given player

Example: “Arsenal’s win percentage this season without Mesut Ozil is higher without him (40 per cent) compared to with him (28 per cent)”

Why it’s misleading: With or without you (or WOWY, as it’s known in sports analytics circles) stats are intended to isolate the impact of a single player in a team to see how results change with that player involved compared to when they’re missing.

These stats can work in sports with smaller segments to analyse such as basketball, which has more line-up changes and is far higher-scoring. In football, however, there are just way too many moving parts for this to be a good way of analysing if a player’s any good or not. There’s too much out of Ozil’s control that he gets penalised for in both situations.

Here’s just a sample of things that ideally should be taken into account but aren’t with WOWY: What was the quality of the opposition? What was the quality of the other players playing alongside Ozil? Was there a red card? Was Ozil subbed on?

Equally, you have the Burnley problem. Ben Mee and James Tarkowski have both played every minute of Premier League football this season. Which is better? We’ll never know.

What to use instead: It’s better to analyse players within the context of their position and focus on just what they can control. For Ozil and other creative midfielders, that’s chance creation, for strikers, it’s goalscoring and so on. Leave WOWY stats to sports played by big lads indoors.

? Thou shalt not judge a player’s pass ability on his passing accuracy

Example: “Phil Bardsley is the worst full-back at passing in the Premier League, completing just 63.6 per cent of his passes”

Why it’s misleading: The degree to which a player’s passing is accurate or not depends a lot on what they’re being asked to do, and the choices they make when on the ball. Some teams, such as Manchester City, play the ball very short and in certain areas of the field, under little pressure. Due to this, they’ll have a high pass-completion rate. Others, like Burnley, look to hit the channels and user longer passes instead of shorter ones — passes that are, on average, less likely to be completed.

The passes may be, by the definition of the data, inaccurate, but that doesn’t tell the whole picture. Consider the example below, from a recent Leeds United game:

https://cdn.theathletic.com/app/uploads/2020/03/22085743/costa_pass_example.mp4?_=1

Here, Helder Costa’s pass goes down as a failed pass into the area but it’s largely due to the excellent recovery run of the Hull City defender. Here, we should care about possession retention and the progression that Costa has enabled. There are various other times when this situation takes place — possession being retained but the pass incomplete — which players get unfairly judged on.

What to use instead: I’ll write more on other options on this in future, as currently I don’t think that there are many metrics that properly cater to this issue. Expected Pass completion rates may give a more rounded view of why a player’s pass completion rates are low but that data is relatively sparse in the public domain.

9) Thou shalt not judge players if they fail a lot

Example: “Trent Alexander-Arnold has made more unsuccessful passes than any other outfielder in the Premier League”

Why it’s misleading: The Athletic’s Michael Cox wrote at length back in January on what being a “failure” in the Premier League means, so I won’t go into too much depth here. The Golden Boot winner every season will fail to score more times than they succeed in doing so. But if we want to find out the most clinical finisher, we’d look at conversion rate and therefore need goals.

What to use instead: In most cases, if the focus is on how many times a player has failed, it’s worth turning that into a percentage to add more context. Have they failed a lot, or is it that they’re tried something far more than other players?

10) Thou shalt not compare players with differing numbers of minutes played

Example: “Trent Alexander-Arnold and James Maddison are the joint second-best chance-creators in the league, with 75 each”

Why it’s misleading: Players who play more minutes have more chances to do things on the field that are counted. By not putting all players on a level playing field in terms of minutes played, it means that those who have played less will nearly always look worse.

I’m probably building some sort of a reputation for always fighting Emi Buendia’s corner but by adjusting for minutes played, Buendia is actually the second-best chance-creator in the league on a per 90 basis (3.3 per 90).

chances_created_table-1024x900.png

What to use instead: By adjusting stats per 90 minutes played (that is, dividing the stat by minutes played/90), players who have played differing numbers of minutes can have their numbers compared, and more fair comparisons can be made.

 

 

    Unwritten: What is speed in football and how do you measure it?

    https://theathletic.com/1703178/2020/03/27/speed-football-premier-league-fast-fastest-player/

    marcus-rashford-speed.jpg

     

    Speed is an aspect of football that is so key, and yet so infrequently written about. There are various ways that teams can use speed to their advantage: attacking upfield fast, getting shots away quickly after turning the ball over, or moving the ball quickly to pull the opposition out of position.

    Rapid movements, fleet feet and quick thinking: the Premier League has always been a league where the use of speed (or lack thereof, in some cases) has lead to greatness — Leicester City’s title-winning season, for example, was built on speed. It is something that’s deemed so important, yet from a statistical point of view it’s rarely mentioned.

    The first kind of speed is the one we all know, and likely had to repeat again and again in physics lessons in school: ”speed equals distance over time”.

    To start measuring how fast a side attacks, let’s first determine what a slow attack looks like. Take Manchester City’s 44-pass goal against Manchester United back in November 2018.

     

    This move took City one minute and 55 seconds from start to finish. They moved the ball a total distance of 699 metres, for a territorial gain of just 42 metres (how far the ball was actually moved up the field).

    Taking the territorial gain and the total duration of the passing sequence, the direct speed (how fast the ball moved upfield) can be calculated. This City goal, while aesthetically pleasing, is on the super slow side, clocking up just 0.38 m/s, and is one of the slowest goals in recent memory.

    If City’s goal that day is the tortoise, the hare is Leicester’s fourth goal against Aston Villa in the 4-1 drubbing back in December:

    leicester_fast_attack.png

    With Ricardo Pereira (21) picking up the loose ball and sending it long, Dennis Praet picks up the loose header from Villa’s Douglas Luiz and sends Jamie Vardy on his way. This move comprised of just two passes (and only one of them is complete, violating one of the commandments) and took 11.5 seconds from start to finish. Moving 86.7m upfield, this goal was reminiscent of Leicester teams of yesteryear, with a direct speed of 7.5 m/s.

    Calculating these metrics over every eligible shot in the Premier League in the past five seasons, we can see which is the fastest attack in terms of raw distance over time. The criteria used here is all shots that have come from open play, that aren’t rebounds, and don’t come from moves that only cover a tiny amount of ground (e.g. a goalkeeper getting tackled and the ball put into an empty net).

    leicester-attacking-speed.png

    Leicester’s title-winning team are the fastest, with moves averaging 3.9 m/s. They also dominate the top five with the 2016-17 and 2017-18 teams included.

    Where Leicester differ, though, is the absence of many passes in their moves leading to shots. Their passes per sequence is the lowest by a long way compared to the other teams (the lowest in the dataset, in fact) and shows how different Claudio Ranieri’s team was when it came to turning defence into attack. Essentially, they moved the ball  by carrying it forward, with Vardy scoring more goals following a carry of five metres or more than any other player in the Premier League that season.

    Sean Dyche’s Burnley in 2018-19 sit third in the table by this measure, with their way of attacking quickly being slightly different to that of Leicester. While Leicester’s quickness was born out of long passing and ball carrying, Burnley progressed their attacks through even longer passes and winning possession from second balls.

    The inclusion of Steve Bruce’s Newcastle United isn’t overly surprising, given the team is set up to absorb pressure and attack from deep, yet their attacking approach is different again. Objectively, they are the most passive pressing team in the Premier League this season, allowing the opposition 19 passes before sticking a foot in and attempting to win it back. They also start their attacking moves from the second deepest position in the league, after Arsenal.

    With Miguel Almiron and Allan Saint-Maximin as the two key attacking outlets in transition, Newcastle look to run the ball upfield, with Almiron and Saint-Maximin as the 3rd and 8th most willing runners in terms of distance-per-carry:

    table_2.png

    Moving the ball upfield with speed is one way that a team can be quick, but another is turning defence into attack quickly and taking shots soon after turning the ball over. Taking advantage of these transitional moments is key to creating shooting opportunities, and speed of thought and speed of reactions are the tools required.

    Defining these moments when teams win the ball back and shoot quickly is relatively straightforward — any shot that comes from the ball being recovered in open play and taken within 15 seconds of the sequence starting is counted. Here’s the fastest teams in the last five seasons in terms of turning defence into attack quickly:

    table_3-1024x979.png

    Naturally, this lists consists of clubs that press high up the field, or at least used to. Jurgen Klopp’s early seasons at Liverpool were when Liverpool’s press was at its most intense, which is a similar story to Mauricio Pochettino at Spurs.

    Manchester United buck the trend slightly. This season, they don’t exactly fit the bill in terms of a high-pressing side, but in Daniel James, Marcus Rashford and Anthony Martial, they have the youngest (and arguably fastest) collection of attacking talent this season. Being able to turn the ball over and attack at speed is another way of getting included here, similar to Leicester’s title-winners from 2015-16.

    Chelsea’s inclusion in 2016 is intriguing, given it’s Antonio Conte first year managing the team, and also the first year of N’Golo Kante in midfield. Eden Hazard had his joint-best year in terms of scoring, as did Diego Costa, as Chelsea cruised to the title.

    Lastly, teams can use speed to their advantage through crisp ball movement — letting the ball do the work — to carve open opportunities to score. There’s the caveat that this measure is far from perfect — with the absence of a timestamp of when the ball is received in the data, these statistics don’t take into account the time that a player is on the ball and the time that the ball is on the move (i.e. has been passed). Nonetheless, the table of fast-tempo sides below makes for interesting debate. To also cater for messier moves in the data (and on the field) only those which last 15 seconds or more are included.

    To approximate tempo here, we take the duration of an attacking sequence and divide it by the number of passes that take place within it. A quicker tempo move is one that has less time between passes. Of two sequences that each last 20 seconds, the one with five passes and a tempo of four seconds per pass is quicker than the other sequence consisting of just two passes and a tempo of ten seconds per pass.

    table_4-1024x945.png

    Arsene Wenger’s final year at Arsenal coincided with Arsenal having the fastest team for ball movement, with an average of 2.8 seconds per pass. Although the passing may have been of the U-shaped, painful variety, it was still done at a relatively high tempo.

    Pep’s City also feature three times in here, which passes the eye test given how they look to pull opponent’s from left to right, waiting for the right moment to play through the lines and carve open a scoring chance. Notably, the tempo at City under Pep compared to Pellegrini has changed a fair amount, with the latter’s side averaging 3.2 seconds per-pass, the highest of any City side in the past five seasons.

    These measures, however, are partly stylistic instead of being indicative of great attacking sides. While Fulham of 2018-19 had a relatively high tempo, and an attack good enough for mid-table last year, the defence was ultimately too flimsy to keep them afloat.

    Speed of movement, reactions, and of the ball itself are just three ways that the fastest teams in the Premier League can be labelled as “fast”. It’s not always a winning strategy to be quick. Sides that play at speed tend to either burn out (Spurs), get tactically worked out by opponents (Ranieri) or lose the pieces that made the speed so successful in the first place (Conte’s Chelsea) — but it’s certainly fun to watch.

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