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Expected Goals: The story of how data conquered football and changed the game forever

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I can't give it full marks because I found it too shallow in its treatment of the metrics being used by the teams to achieve their goals. I didn't expect any math of formulas per se, but maybe more elaborate definitions followed by concrete examples would have sufficed. xG, or Expected Goals, is a methodology formulated by Alan Ryder, who wanted to measure the quality of a shot at goal, but not in Football. No, xG started when Alan stated: Not all shots on goal are created equal. I can't even figure out who could be a good audience for this book. An average fan, trying to dip his toe into advanced statistics? The book litters him with way too much numbers while teaching precious little. Someone, who is more interested in the depth of football and/or advanced statistics? The deepness of the actual coverage of xG here is extremely shallow, offers almost no insights on modeling, mathematical or any other level. I can't picture anyone who would like more than a third of this book.

It doesn’t help that when the book came out, Liverpool promptly decided to be terrible,” he adds wryly.An interesting concept mentioned in the book is the Justice Table. The Justice Table ranks teams in a division according to the Expected Points (xP) they have accumulated over the course of the season. Before long, football clubs had caught on to the ground-breaking insight given by xG. Brentford FC were leaders in this field, managing to assemble a Play-Off-reaching squad on a shoe-string budget. In the last five years, the small West London side have turned over more than 100m in transfer revenue from their use of the Expected Goals method in player recruitment. However, some players have still consistently outperformed their xG over the same period, most notably Lionel Messi, Harry Kane and Sergio Aguero. If you ever want to assess a player’s skill in relation to the average player, then it’s important to use a large enough sample size to omit any variance. clubでは行動経済学者、統計の専門家、数学者や気候学者、どれも確率論に習熱した新手の人物たちが、データ分析の結果をもとに、監督に代表されるサッカー業界にどっぷりつかった伝統主義者と、時にはバトルを、時には助け合い、このサッカーというゲームの潮流を作っているというのだ。 The book uses a surprising amount of anecdotes to talk about a subject which is about large aggregated data. The scouting chapter focuses almost entirely on one club that uses xG; the player xG chapter focuses on certain players like Ronaldo, Hazard, Mane, and a few others; the first half of the book seems to revisit the same Arsenal-Manchester City match a thousand times. Expected goals is a statistic based on aggregated averages, so why not use more examples that incorporate that? Ironically, the author seems to bring up more examples that contradict the power of xG as a predictive tool (like the aforementioned Arsenal-Manchester City match), so, since the author never shows sufficient empirical support for xG as a predictive tool, the anecdotes included in the book only made me more suspicious of xG. It really is laughable.

This statistical approach is especially useful for properly rating teams and players. From a club’s perspective, holding more reliable information about players can help it maintain a competitive advantage by identifying proverbial diamonds in the rough when scouting players. The increased exposure which the metric has seen over the last couple of years is simply a drop in the ocean of what is to come. The reason why is simple: Every footballing judgement ever made is based on an analysis of the performance of teams or players. And the Expected Goals method offers by far the most advanced, profound and accurate gauge of performance. Clubs will want their athletes in tip top shape for matches and they can only be sure that their athletes are in the best physical condition by using the data at their disposal to keep an eye on their performance in training.xG is a metric that goes beyond the traditional shot counts, but it is important to remember that it is still just a metric. We can use it to evaluate underlying performances, but it is actual goals that are going to win you football matches. Identify performing players in underperforming teams, or those who receive less playing minutes, by assessing which ones are more effective than the quality of their chances they receive would suggest. There is something self-contradictory about The New York Times journalist Rory Smith’s recently released book, Expected Goals. He charts the data revolution in football, the way statistical analysis has become central to how clubs operate. Yet, this narrative on numbers is completely bereft of graphs and formulae of any sort. It’s what initially drew me to the book. To preserve the benefits of the depth of historic data in the men’s competitions, the women’s model is powered by the same features described above but it is re-trained on relevant data across nine major women’s competitions between 2018-19 and 2021-22.

Tippet explains how XG is calculated and why this statistic is important in analysing football results, teams and players. The Expected Goals Philosophy is a very helpful read for anybody wanting to use expected goals to inform their betting or simply learn more about it. Tippett successfully makes a complex concept easy to understand and uses a broad range of examples to explore the benefits it provides for expanding your understanding of soccer. The parts about gambling and how to make money were the least interesting as that isn't why I watch football. It seems like only the rich syndicates will ever make.money from that.While watching a game, we can intuitively tell which chances are more or less likely to be scored. How close was the shooter to goal? Were they shooting from a good angle? Was it a one-on-one? Was it a header? Using 21 st Club’s tool – which calculates the historical link between a player’s on pitch actions alongside overall team performance level and subsequently assigning an overall rating – a player who was earning 25% less than the average player on the client’s shortlist and who was better than all but one on the said shortlist, was identified.

It’s a sentiment that resonates with me. Over the last couple of years, I have developed a disdain for the suffusion of data ino the sport, primarily born out of my inability to understand or appreciate the changing lexicon of footballing discourse. Companies like STATSports are making significant sums as their GPS sports vests are used by some of the biggest teams in world football as a way of measuring and monitoring the fitness of players. As an Argentinian, I have been a football fan (it's actually spelled Fútbol, but whatevs...) my whole life. It is our first love: Fútbol, then our mothers (and believe me, they are well aware of their place). Really interesting and accessible introduction to expected goals. I managed to get through the book in a 24 hour period. I was thoroughly gripped and learnt a lot. I found the comparisons between Moneyball and Brentford revealing and insightful, particularly how Brentford have utilised xG to exploit the transfer market. Tippett explores the differences between results and performance in an engaging and easily understandable way. The graphics, data and presentation of maths was easy to follow. Despite this, most ordinary fans still don't understand what the Expected Goals method is - or appreciate the significant impact that it is set to have on the sport in coming years.

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Overall: An interesting book, which could have been much shorter to cover the material included or could have been more in depth. At times it felt like there was a lot of padding in the prose to get to some sort of pre-ordained word limit. The subject material is interesting, however I was left wanting more detail. So a good first effort in an interesting subject with room for the author to grow in future. Worth a read. Also, while we know that Beane's As achieved incredible success as underdogs in a league that was designed to allow money to pretty much buy titles, the same cannot be said for most of the teams examined by Smith. Yes, some of them achieved immense success, but they were also the same teams that had access to astronomical amounts of money. How much money do you ask? Oil money, buy-the-metrics-company-so-that-no-one-knows-our-secrets money. But that is also part of the Moneyball way of doing things, according to Beane himself; that record-breaking signing makes sense if the data says the value is there, bargain or not. Many have described this as the worst individual performance by a striker in World Cup history…and it’s hard to argue when you see the chances he missed.

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