FPL Gameweek 2: Assist Kings

Tom Thomas
Fantasy Tactics and Football Analytics
9 min readSep 25, 2020
Wilfried Zaha glides past Victor Lindelof (Manchester United vs Crsytal Palace) source

What an entertaining week we got to encounter. Results aside, the weekend was filled with so many goals. Goals galore and they were quite a few sublimes ones as well. 44 Goals! The last time we had such a high-scoring weekend would take us all the way back to February 2011 when 43 goals were scored in a single Gameweek.
More goals means more assists too and of course, more points, especially for those who chose correctly. The most incredible duo was Heung Min-Son and Harry Kane who exchanged 4 Goals and Assists between them. To top it off, Kane scored another one as well before he was substituted off.
If you’ve been following along, you know that bears great news for us as our team did include Harry Kane in the previous Gameweek. But did I keep him or make the incredible mistake of swapping him out? There were several other high scoring games as well with Leicester beating Burnley (4–2), Leeds and Fulham going back and forth with 7 goals between them (4–3), and Everton thrashing West Brom (5–2). The link to Gameweek results can be found here.
Let’s take a look at our team selection.

The Results

If you take a close look and compare the team below with our previous one, you’ll notice that I did indeed make a transfer for this Gameweek. The decision was to take out Roberto Firmino for Anthony Martial. A decision that did not pan out well.
The primary reason for this change was the opponent that Liverpool faced over this Gameweek — Chelsea. Considering the difficulty of this game and taking into account that Manchester United had a relatively winnable game against Crystal Palace, a dilemma was posed whether to keep Firmino in the team or not.

Fantasy Analytics Team Gameweek 2 (Left) and Kings of Gameweek 2 (Right)

According to FBREF, Martial had the highest np:G-xG score among all the United players for the 19/20 Season when it came to attacking production. np:G-xG calculates the value of a player when contributing to non-penalty goals excluding expected non-penalty goals. Quite simply, the most accurate number of non-penalty goals one could see a player scoring in a game. Considering these stats, a decision was made to choose him over Firmino.
Granted, he wasn’t a part of our strategy to choose players only from the list of teams we decided on. So this was a human error on my part. As we see, selecting him didn’t pan out well, especially since I chose him to captain my team as well. However, considering his valuable stats and the risk we took, I’m going to consider keeping him in the team until we get the most out of this transfer. Gameweek 3 presents an opponent in Brighton for Manchester United.
It also shows us that depending on the value of one single stat may not always result in a positive outcome. This is true whenever dealing with datasets.

On a positive note, the rest of the team did quite well. Below, we compare Gameweek 1 outcomes to this past weekend and we can clearly see a jump in our total points. Last weekend we were 11 points below the mean and this week, we have jumped up to 12 points above the mean. So with this weekend, we’ve made up for Gameweek 1 and can strategize with a clean slate of production for Gameweek 3.

Comparing Gameweek 1 and 2 Results

Taking a look at our cumulative points total within each part of the team over the two Gameweeks, we can see a simple breakdown as so:
Goalkeepers - 8 pts out of a possible 24 pts. (33.3% Efficiency)
Defense - 37 pts out of a possible 104 pts. (35.6% Efficiency)
Midfield- 23 pts out of a possible 103 pts. ( 22.3% Efficiency)
Forwards- 43 pts out of a possible 84 pts. (51.2% Efficiency)

Observations from our selections

  • The highest-scoring players in our Team have been Harry Kane (21 pts), Richarlison (12 pts), Harvey Barnes (13 pts), Michael Keane (7 pts), and Virgil Van Dijk (7 pts). One thing to note is that all of these players except for Van Dijk came from our initial strategy teams — placing further value on choosing players from teams with easier fixtures vs. just the best players (Martial).
  • Last week, we analyzed Harvey Barnes as an excellent choice to keep despite his measly return of 3 pts. This week, our hypothesis on being quite active on the field was repaid with 13 pts. If you watched the interview with Joshua Bull that I shared last week, you’ll have noticed that he shared info to a common database that Vaastav Anand had compiled gratefully for public use. This data included all stats that FPL themselves provide via the FPL website. Here’s a link to it if you’d like to do your own data crunching.
    Taking a look at this data, one of the key stats that FPL calculates is creativity based on their own ICT Index.
    According to FPL, the definition of the ICT Index is that it’s a “statistical index developed specifically to assess a player as an FPL asset, combining Influence, Creativity and Threat scores.”
    The Creativity stat is FPL’s own analysis on the rank to provide each player based on the amount of goal-scoring opportunities they add to the team. Taking a look at Barnes within the Leicester team, we get the following result. We compare the cost of a player to the amount of actual value they provide to the team. We can see that despite being a much cheaper player than Vardy, Barnes adds much more value to the team.
Harvey Barnes vs Leicester Gameweek 1 source
  • Taking a look at our Team efficiency, we can see that our weakest area is in the Midfield. Let’s take a look at why this maybe.
    1. Lucas Moura (TOT) - Fixture difficulty has been a significant issue for this interesting season we have in 2020 so far. Covid-19 has affected the schedule of many other games that surround a regular Premier League Season. This has led to multiple players not getting the starts that they usually might out of injury concerns. Moura is one example. Spurs have one of the most games to play in a short amount of time. This is one area we didn’t take into consideration. Our fixture calendar only analyzed Premier League games. We can learn from this situation that it maybe better to choose players who only have an absolute chance to start every game, for example, Harry Kane vs. fringe players such as Moura.
    2. Diego Jota (WOL) - Covid-19 has again posed an issue here. Due to the truncated offseason, the Premier League decided that the transfer market would run into October as opposed to being closed off much early during a regular season. We questioned why Jota wasn’t selected last week by Nuno Espirito Santo since he was such a key player to the Wolves lineup. Turns out, he was undergoing transfer negotiations with Liverpool who purchased the player for £41m in the past week. Sigh, this unexpected situation has cost us some points but could be a blessing in disguise. If Jota goes straight into the Liverpool lineup, we could see some valuable points being picked up from him considering that he is in a stronger team now.
    3. Adama Traoré (WOL) & Abdoulaye Doucouré (EVE)- These are the two weakest points in our defense that may need changing. Arguments could be made for Doucouré since he is in a new team and has moved to a more defensive role compared to his role while he was at Watford. However, Traoré is simply not doing well enough and seems to be going through a bad spell of form. We will be analyzing whether to keep these players or not in the upcoming Gameweeks.

Valuable Picks for Gameweek 3

In this section, we’ll analyze the trends we see over the past 2 Gameweeks going into Gameweek 3 to decide who may be your best picks if you weren’t limited by any strategy such as the one I’m taking currently.

  1. Taking a look at the most highly-rated players based on FPL data and considering only those players who won’t break your bank (<£8), we get the following results, These players currently offer you the best bang-for-your-buck. However, be wary of who they play in the coming Gameweek. Of the following players, Rodriguez or Calvert-Lewin may offer you more value as they face an easier opponent in Crystal Palace. However, Palace seem to be on a good run of form at the moment so don’t count out Zaha just yet.
ICT Index vs Current Cost source

2. Now let’s take a look at another interesting stat. If we account for just the starters over the past two Gameweeks who have been the most effective in getting assists and goals and we combine that with the number of times a player was offside, we get the following outcome.

Offsides vs Goals & Assists per 90 minutes source

At first glance, you may ask- what does being offsides and bagging points have to do with anything? the answer is positioning. These would be the players that knew where to be on the pitch during crucial moments. In particular, consider Son Heung-min as an example here.

You can see a clear example of how well he drives forward between the lines while waiting for that perfect pass.

3. Finally, take a look at these Penalty Kick numbers so far. These have been the designated kick-takers so some valuable points are to be gained from them. However, out of these players, only Neal Maupay and Wilfried Zaha were on the field the longest indicating how important they are to their respective teams.

Penalty Kicks vs Goals per 90 minutes source

Good luck with your Gameweek 3 picks! We will continue next week where we left off and analyze how we performed in Gameweek 3.

If you’d like to follow along in this series, here is a timeline of the FPL data analytics project.
1. Introduction
2. Gameweek 1

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And there we go! Hope you enjoyed reading this piece as much as I did writing it. Feel free to write down any thoughts or suggestions. I’m always working on improving my analysis and my articles as time goes on so I appreciate all comments! Collaborating is fun so if you’ve got any interesting projects in mind, feel free to reach out to me personally. If you would like to follow me to keep up with updates in this series, follow me here or — — @__tomthomas

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Tom Thomas
Fantasy Tactics and Football Analytics

I geek on stats • Certified IBM Data Scientist • Industrial Engineering Background • Exploring the power of data • tomthomas.github.io