Could Data science be the key to having a winning fantasy football league? Fantasy football operators would provide you with all the details you need to win the match, after all, the stats stay the same for you and the person who is winning the league day after day. 

What is the difference then? It is the strategy they use while making the selection, suppose the all-rounder you chose initially had injured himself right before the match. The good news is you have ample amount of time to pick a substitute but who would you choose? Oftentimes people who are new to the fantasy sports industry would end up choosing a familiar face with good stats and choose the player on ‘gut feeling’. This is the difference between you and the person who is winning consistently in the fantasy league.  

Giving emotions some weightage before making a decision could be the reason why you are not getting the results. While it could be argued that many people have been successful with their method but experience also counts. The sure shot way to have a winning fantasy league is to use data science to evaluate potential players first. This could give you an edge even over some experienced players and in the long run Data can complete with a gut feeling as well. 

Many players have adopted this approach while creating their own rosters for the league and over the course of the season have had success and even create an undefeated system. While their methods took years to perfect, one such player took 15 years to have a winning fantasy league formula but we have a secret too. After using a variety of methods, players' performance predictions is the one that truly stands out and has been tested many a time. This would give a rank on how the player has performed on the field over the season. Another is propensity score matching as well where a list of players is matched with historic players based on their way of playing and other parameters as well. 

There is a way to even compare the stats of the player and rely on the results of other players who have been in a similar situation. Suppose in an NFL match a new player would not have a lot of stats that could be used to predict how he would perform but his stats could be compared to other rookie quarterbacks who had similar stats as the new player in the first few matches and later on you can get more data points once you compare how the five rookie quarterbacks performed next year in similar circumstances. This method would help in creating a predictable system. Data science thus does offer an edge to the players at least over the users who use emotions while making a decision in the fantasy football league. With Data Science you can separate emotions and rely more on the numbers to give you a clearer picture.

What are the other methods of analysis one can use? 

First is Linear Regression, with this method you can see how a dependent variable would perform against one or more independent variables. This method could be used for forecasting, if one wants to see the data points for how a football player will perform in the future past values can be used to chalk a path. 

Next is Neural Network, this method needs a lot of data to work with but it would help you reach a conclusion at the end of the analysis. When one deals with a lot of variables at once and there is a chance of possible noise in the data Neural Network could give the best possible result in such a case.

Lastly, Random Forest method, you can solve several problems with their method as it follows an ensemble approach. As oftentimes in many situations there is not just one right answer there could be many and hence with this method you can get as many trees as you need and the tree which holds a host of results would give you your winning strategy. 

With the data science approach, you could also factor in a player's health while making a decision as it would be hard to gauge the exact effect of the health without data. Further weather could be factored in to make a more complex system and keep a track of opponents' performance as well. 

Even the big sports companies rely on a data scientist to produce their own player rankings, this approach could truly take your game to the next level. 

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