The stats that I am using to project player stats are Hits (H), Runs (R), Homeruns (HR), Walks (BB), and Strikeouts (SO). I will look at each batter

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3 The stats that I am using to project player stats are Hits (H), Runs (R), Homeruns (HR), Walks (BB), and Strikeouts (SO). I will look at each batter on a 25-man roster and average their total H, R, HR, BB, and SO from the past 3 seasons, and these averages will be their projected stats for this season. I will also look at each pitcher on a 25-man roster and average their total allowed H, R, HR, BB, and SO from the past 3 seasons, and these averages will be their projected stats for this season. To take into account player decline and improvement, I will scale each projection by taking into account their age. Assuming that the peak age of a player is 29 years old, I will multiply each projected total by (29)/ (current age) to inflate numbers for younger players and deflate them for older players. 25

4 After having this data set of player data for all the batters and pitchers on each 25-man roster, I will group all the players together based on which team they are on, and total each team s players stats. From here, I will have a data set of projected team stats for all 30 teams this season. I then retrieve a dataset of these same team stat totals from all teams over the past 3 seasons, as well as their win totals, which will result in 90 total possible teams. In order to avoid stat inflation between seasons, I will rank the projected stats from 1 to 30, and then rank the stats from the previous seasons from 1 to 30 ranked against the other teams in that particular season.

5 At this point I will have two necessary datasets: Dataset 1: all 30 teams rank for each projected stat total for this season. Dataset 2: all 90 teams rank for each stat total from all teams over the past 3 seasons, as well as an extra attribute with each teams win total that season. From here, I will use a modified k-nearest neighbor algorithm to project the win totals for each team (coded in Java): For each row in dataset 1, I will calculate its k-nearest neighbors in dataset 2, using the Euclidian distance. Once I have the k-nearest neighbors, instead of clustering it like normal, I will just average the win totals of each k- nearest neighbor, and this average will be the projected win total for that team. Potential example for k = 5: 2015 DET 2015 TOR 2016 BOS 2018 SFG 2017 COL 2017 NYY 2017 ARI 2015 NYM Current entry to project Average the wins of these teams to project win total 2015 PIT 2017 LAA 2015 LAA

6 My final product/results will be a file that displays the projected standings for the 2018 season based off of these projected win totals. Unfortunately, the bulk of my work for this project is the data retrieval. I am still currently in the process of retrieving all of the data, and so therefore am unable to share any simulations or display my final results for this project. I am getting all of my data off of which conveniently has all the stats needed and even allows exportation directly into files.

7 As far as evaluation, it would be impossible to evaluate these projections until the end of the season, when the teams win totals are finalized. But in order to avoid waiting until October to evaluate my projections, I will use this exact technique to also project the 2017 win totals, which I can evaluate against the true win totals from last season. To judge error, I can use a simple percent error with the following: % error = 100 x (projected wins actual wins)/ actual wins

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