Wonderkids Wonderkids
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Accelerating the adoption of data analitycs in football WONDERKIDSWONDERKIDS 2021 - Outlook January 2021 1 Who we are Data gathering solutions We are a Milan-based startup with the In our view, at the moment the most scalable ambitious mission of accelerating solution to gather performance data is the adoption of data analytics in represented by wearable technologies. football and bringing a higher degree of objectivity to the Beautiful Game. In five to ten years, artificial intelligence will probably permit reliable video recognition Our ultimate goal is to reach all tiers of and any event happening on a football pitch football, including non-professional players will be captured and analysed through a and coaches. We strongly believe that a smartphone (video analysis is currently done more widespread adoption of unbiased with human intervention and is therefore not methodologies for evaluating performances scalable). would greatly benefit the sport we love in the long term. We are not there yet though and, for the time being, wearables seem to us as the only In order to achieve our mission, we are applicable methodology to gather data at focusing on two main tasks: scale. In the past three years we have been developing our first wearable device, a pair of 1. developing the most scalable and smart shinguards able to track the technical automated solutions to gather football events of any football match or training statistics; session, on top of the athletic performance. 2. designing the most compelling, user- For more information visit soccerment.com/ friendly and intuitive analytics tools and wearables or contact us directly (sales@ extract the highest level of intelligence soccerment.com). from performance data. Accelerating the adoption of data analytics in football 04 Soccerment in a nutshell 2 3 Data analytics Research and advisory Data alone isn’t enough: you need analytics. There are two other activities which create For example, if football clubs are flooded interesting synergies with the aforementioned with huge streams of numbers, but lack the tasks: research and advisory. internal know-how to interpret them and extract actionable information, data becomes Research is a fundamental part of what we almost pointless. It’s like giving all the prices, do, for two main reasons. On the one hand, ratios and indicators on stocks, currencies it increases our internal knowledge and and commodities to a person who doesn’t stimulates us at developing new algorithms know about financial markets: the data itself and data visualisation and forces us at doesn’t make that person an infallible trader. continuously improving the outstanding To make good decisions, football teams need ones. On the other hand, it allows us to data, of course, but they also need analytics to exchange know-how with the outside world. make sense of it. For instance, our research blog, active We believe that in five to ten years all the top since 2017, has given its small contribution football clubs will probably have the internal to popularizing football analytics concepts know-how to easily understand the most and has created some stimulating chances to advanced stats and extract valuable insights debate them. from complex raw data and that by 2050 this will likely apply to any football club (including Advisory is an activity which is very much non professionals). connected to research. As we discover more and more about how data can help a football However, at the time of writing, the situation club at making better decisions, our know- is different. Most professional football clubs how becomes helpful to various entities in the do not showcase structured teams of data football world: the clubs, obviously, but also scientists and their decision makers need many other actors gravitating around them, actionable information. This is why we including players, scouts and player agents. are putting so much effort into analytics and find solutions to intuitively show data If you are a football professional and would and easily evaluate and compare players. like to know more about our advisory Our analytics platform makes intensive services, feel free to contact us via email use of data visualisation to comprehend ([email protected]). football players in a few seconds, despite skimming through a vast amount of data (analytics.soccerment.com). Soccerment Research - Wonderkids: 2021 Outlook Contents (as displayed in the original document) 01. 6-9 The project 02. 10-15 The methodology 02.1 Initial List of 100 Players 12 02.2 Ranking methodology 14 03. 16-117 Wonderkids analysis 03.1 Summary table 18 03.2 Adil Aouchiche (AS Saint-Étienne) 20 03.3 Harvey Elliott (Blackburn Rovers F.C. - on loan from Liverpool F.C.) 24 03.4 Nuno Mendes (Sporting CP) 28 03.5 Rodrygo (Real Madrid) 32 03.6 Nathan Collins (Stoke City F.C.) 36 03.7 Pedri (FC Barcelona) 40 03.8 Adrien Truffert (Stade Rennais F.C.) 44 03.9 Jude Bellingham (Borussia Dortmund) 48 03.10 Mason Greenwood (Manchester United F.C.) 52 03.11 Mohamed Ihattaren (PSV Eindhoven) 56 03.12 Rayan Aït-Nouri (Wolves - on loan from Angers SCO) 60 03.13 Noni Madueke (PSV Eindhoven) 64 03.14 Myron Boadu (AZ Alkmaar) 68 03.15 Bukayo Saka (Arsenal F.C.) 72 03.16 Giovanni Reyna (Borussia Dortmund) 76 03.17 Flavius Daniliuc (OGC Nice) 80 03.18 Jérémy Doku (Stade Rennais F.C.) 84 Accelerating the adoption of data analytics in football 03.19 Odilon Kossounou (Club Brugge KV) 88 03.20 Florian Wirtz (Bayer 04 Leverkusen) 92 03.21 Eduardo Camavinga (Stade Rennais F.C.) 96 03.22 Curtis Jones (Liverpool F.C.) 100 03.23 Lassina Traoré (AFC Ajax) 104 03.24 Ryan Gravenberch (AFC Ajax) 108 03.25 Benoit Badiashile (AS Monaco FC) 112 03.26 Ansu Fati (FC Barcelona) 116 04. Soccerment 120 Soccerment Research - Wonderkids: 2021 Outlook January 2021 02 THE METHODOLOGY 10 Accelerating the adoption of data analytics in football January 2021 time constraints, which ensure statistical Initial List of Around relevance, drastically reduced the number of 100 Players players that we could include in our Outlook, which means that we had to exclude some very interesting prospects. Here below is a partial list of some of the most interesting We took into consideration all players who U-19s that were excluded from our Outlook, were born on 1st January 2001 or after due to time constraints (data related to the and who – as of 31st December 2020 – had leagues we cover, as of 31.12.2020). played at least 500 minutes in total and at least 270 minutes in the current season, in one of the fourteen leagues we cover (tiers 1 and 2 in England, Spain, Italy, Germany and France, and tier 1 in the Netherlands, Portugal, Belgium and Turkey). The playing Figure 01 - List of some of the most interesting U-19s that were excluded from our Outlook, due to time constraints | Data related to the leagues we cover, as of 31.12.2020 Accelerating the adoption of data analytics in football 02. The Methodology We thus easily filtered the top 50. Here are Ranking methodology some statistics regarding the top 50 list: • the average age is 18.6 years; The methodology by which we arrived at the • the most represented leagues, with 8 wonderkids’ final ranking was divided into players each, are: La Liga, Ligue 1, and the two stages. Jupiler Pro League; • the most represented nationality is First step: selecting the best 50 French (8 players); • the most represented clubs are Rennes The first stage consisted in ranking the and Club Brugge (3 players each). initial list of around 100 players by using our Soccerment Performance Rating (‘SPR’), the algorithm we have developed to evaluate the performances of football players. SPR focus The SPR takes into account every match event, weighted through ad-hoc coefficients. The various events (rebased to ‘per 90 minutes’) form the contribution of a player to the defensive, buildup and attacking phase of her/his team. These contributions are then weighted according to the player’s role (for example, the attacking contribution will weigh more for a forward and less for a defender). In line with what we do for the overall stats, we show the SPR of a player when she/he has accrued at least 270 minutes of playing time. In order to reward consistency of performance and limit short-termist hype, the SPR is adjusted by taking playing time into consideration. At 270 minutes of playing time the adjustment is 84.7%. It reaches 100% when the footballer has played at least 1,800 minutes in the league (in most leagues, this equals half a season, plus one match). Therefore, every additional minute above 270 and up to 1800 minutes is worth 0.01% of SPR. It is worth mentioning that the five players with the highest SPR at the moment are (in terms of total SPR, i.e. the weighted-average SPR for all the seasons available in our database): • Lionel Messi, with 84 • Neymar Jr, with 76 • Cristiano Ronaldo, with 75 • Robert Lewandowski, with 70 • Kylian Mbappé, with 70 A total SPR of 70 is the threshold that we use to define the so called “world- class” players, while for the “elite players” the threshold was set at 60. It is also worth mentioning that the maximum level of a seasonal SPR was set at 100 and corresponds to the performances reached by Lionel Messi in the 2011/12 La Liga: 50 goals (plus 8 shots against the woodwork), 16 assists (from 92 chances created), 176 successful dribbles, almost 2000 accurate passes, plus other kind-of-unreal stats.