This project scrapes the official Fantasy Premier League stats, and advanced soccer stats (such as xG) about teams and players from Understat.com. In addition, it merges data from these two sources into, and uploads it to a Heroku PostgreSQL database directly from Python.
This data is very valuable for better understanding of the English Premier League, the Fantasy game and most importantly - soccer in general.
Which are the players who makes the maximum from their goaling opportunities? This question is of great interest for football managers, and rightly so. Luckily, the development of the xG stat made this pretty easy. For each goaling opportunity the xG returns a number between 0 to 1, representing the probability to score. And so, by comparing the xG of each player to his actual goals scored, we can get a solid quantitative value for this metric by subtracting the xG from the Goals made. Another way used here is to plot the two variables. The more a player is above the regression line the better he is in seizing opportnities, and on the contrary. From this plot (data due to GW19) we can learn, for example, the Heung Min Son and Kevin De-Bruyne - two of the best players in the EPL - have about the same goaling opportunities, but Son uses those much much efficiently than De-Bruyne.
Fantasy players deal with tight budget constraint, and as so it is of great importance to pick cheap players that produce many points. As before, It can be learned by scatterplotting the two parameters (cost and points) and checking who is above the regression line (high value) and who is under it (low value). Here we can see, for example, that although Moe Salah is the most expensive player in the game he is still of great value. On the other hand, the disappointing season of Aubameyang is reflected in this chart as well, as he is one of the most expensive players in the game but doesn't produce that many points.
Another feature which fantasy players are highly interested in is stability, or in more formal statistic language - low variance. A player which scores every 10 games a hattrick may have a lot of points, but it is very hard to predict when will he get them. To this end I've created a stability index. It is caculated by taking the standard deviation of each player's points variable, multiplying it by -1 (so the lowest standard deviation will be the highest number), and then passing it to a MinMax scaler, producing a range of value between 0 (the least stable player) and 1 (the most stable player). This table, for example, presents the stability index of the top 20 stable players due to GW19 with a least 4 points average per game
Head to head comparison between two player is often required, and therefore it is handy to be able to easily compare them along several parameters. To that end, a radar plot is absolutely perfect. This one, for example, compares between two of the best players in the English Premier League: Moe Salah and Kevin De Bruyne. The plot - presenting data until Gameweek 19 - shows clearly that while Salah scores more and has more fantasy points, De Bruyne assists more and is more stable. This allows the Fantasy player to decide which goals, and make an informative decision which of the players is better suited to reach these goals.
