Fantasy Premier League (FPL) performance predictors tend to base their algorithms purely on historical statistical data. The main problems with this approach is that external factors such as injuries, managerial decisions and other tournament match statistics can never be factored into the nal predictions. In this paper, we present a new method for predicting future player performances by automatically incorporating human feedback into our model. Through statistical data analysis such as previous performances, upcoming xture di culty ratings, betting market analysis, opinions of the general-public and experts alike via social media and web articles, we can improve our understanding of who is likely to perform well in upcoming matches. When tested on the English Premier League 2018/19 season, the model outperformed regular statistical predictors by over 300 points, an average of 11 points per week, ranking within the top 0.5% of players - rank 30,000 out of over 6.5 million players.
Fantasy premier League (FPL) 1 is the o cial fantasy football browser game for the English premier league, with over 6.5 million players actively competing against each other every season aiming to accumulate the highest number of points. Fantasy sports are continuously becoming more popular, with Fantasy Premier League leading the way in terms of number of players per season. Because of this rise in popularity, achieving a respectable rank is becoming increasingly di cult, much less trying to win on either the o cial or paid leagues.
In this paper we present a novel approach for recommending FPL player selections for upcoming gameweeks that automatically incorporates human feedback into the model through publicly available online data such as blogs written by domain experts (Fig. 1. By considering news articles, fantasy-football prediction blogs and even tweets using fantasy premier league speci c hashtags, our recommender system is able to consider feedback from experts and fans alike. These are used as additional parameters in our predictive model that enables our model to automatically incorporate external factors such as midweek game lineups and performances, managerial decisions, rotations and injuries together with unexpectedly good or bad performances in previous weeks. We found that incorporating di erent data sources signi cantly improved our various models nal rank. The statistical model placed within the top 30% of players, improving to top 0.5% overall rank (within top 30,000 out of 6.5 million players) when incorporating data from betting markets and blog posts. Our recommender system is the rst of its kind to quantify the added value of incorporating public textual data into our predictions. 2
The general inclination observed in previous works has been to use purely
historical statistical data in combination with Naive-Bayes, SVMs, Random Forests
and other ensemble methods to predict future player performances. Using strictly
historical statistical data to train a Gaussian Naive Bayes algorithm, Thapliya
was able to predict future player performances with a reported accuracy of 81%
[
Raghunandh reported that while the di erent tree models all proved to be
effective, gradient-boosting machines (GBM) proved to be the most optimal for
fantasy sport predictions [
Multiple di erent approaches were considered and tested. Most notably di
erent machine learning algorithms including SVMs, Random Forests and GBMs
showed the most promise. However, GBMs were nally picked due to their
robustness, adaptability and proven success when predicting future FPL scores as
recommended by Raghunandh [
The upcoming xtures market odds are then scraped and the odds converted to likelihood probabilities. Player speci c markets such as "player to score" were not available but could have helped in identifying the individual probabilities rather than the entire team scoring probability.
It is important to note that the rules followed by the models di ered slightly from the real game. Since we present a recommender system, our model selects what it believes to be the optimal lineup every week, but real FPL players are restricted and can only transfer one new player per week without any point penalties. This di erence means that our recommender system should be used as an assistant providing accurate recommendations per week.
The nal GBM was evaluated by comparing the number of points it could achieve in individual gameweeks to purely statistical models - representing the baseline. As shown in Fig. 6, the model that considers all the di erent data sources outperforms the statistical model. Starting out more or less similarly, the model bene ts from signi cant improvements as the season continues on, achieving an average score of 63 points per gameweek while the baseline (statistical) model only scored 52 points. Our model achieved 2314 points during testing, placing it within the top 30,000 players out of over 6 million players this season (top 0.5%). The baseline model reached 1994 points ranking it just below the 800,000 mark (top 13%).
Both models perform weakest when picking goalkeepers, with the statistical model performing slightly better having an overall precision of 82% against the multi-stream models 79%. This is likely because goalkeepers tend to be very consistent and heavily reliant on the team's overall form. This is shown in Fig. 5 which highlights the most important variables during goalkeeper predictions. On the contrary, when looking at forward predictions, both models perform exceptionally well, with the baseline predictor achieving a precision of 88% and the multi-stream predictor a precision of 92% when tested on individual gameweeks.
We found that standard NLP libraries could not properly identify footballrelated sentiment in tweets. A corpus built using all the previous seasons FPL related tweets could have been one possibility to solve this issue, allowing us to incorporate tweets into our di erent models. Overall, we have observed signi cant improvements in this approach when compared to the baseline models, especially further into the season when more data is available. By properly understanding the strengths and weaknesses of each of the di erent data sources, the recommender system can be ne tuned to surpass the baseline models in all the positions. We now understand that statistics are much more important for goalkeeper selection than article recommendations, so a higher weighting should be applied in this scenario. In this research we show that Multi-stream data analytics is a viable concept and can be expanded to work not only for fantasy sports predictions but also for all other areas in which both statistics and human feedback are important factors; such as for example in stock markets or election voting predictions. 5
The primary research aim of this work was to investigate whether combining data from di erent data sources would help in increasing the accuracy and overall performance of predictions. Ultimately, we found that while the accuracy of predictions su ers slightly (from 91% to 89%), we found that the average points achieved per gameweek increases signi cantly from 54 to 64 points per gameweek. This is a signi cant points-increase, resulting in a rise in FPL leader board rankings from rank 800,000 (top 13%) to rank 30,000 (top 0.5%) out of the 6.5million total players. By properly understanding the strengths and weaknesses of each data-source, it becomes easy to solve such issues by simply applying more weight importance to statistical parameters when considering goalkeepers.
In this paper we aimed to combine statistical metrics with betting odds, news-articles & blogs, and Twitter data to improve FPL recommender services. We found that the inclusion of both betting data and news-articles & blogs individually, signi cantly improves on the power of predictions made by statistical predictors. However, by including both sets of data simultaneously, the predictions become considerably more powerful than their individual counterparts. This indicates that the incorporation of any relevant additional data would have positive e ects on the predictive power of such recommender systems. While we also investigated adding social media data through Twitter, we faced many challenges leading to inaccurate predictions a ecting the overall predictive power for the worse (Fig. 4. By creating a custom corpus based on the previous years of FPL data, FPL-speci c social media could have a signi cantly bene cial impact on our model. This is because of the vast amount of discussions and independent opinions that are spread through such sites. Another alternative could have also been the FPL-speci c Reddit discussion board 8.
Naturally, while this work was focused on fantasy football predictions, the methods described could be used in any domain that relies on statistics but also
on human feedback; for example stock-markets or even political campaigns both of which bring about lots of social-media data.