Week 6

 

This week I worked on a model to apply to last season's playoffs in order to see how its predictions compared to the actual outcomes of the first round of playoff play. The model that I was assigned used different parameters to determine expected goals (xG) for teams depending on whether they were hosting the match or were the visiting team. For the 6 home teams in the first round of the playoffs, the model only considered goals scored while playing at home throughout the regular season for its xG calculations. For the 6 visiting teams, the model only considered the goals scored in away games that season to calculate xG. This model accurately predicted the result for four of the six matches in the first round of the playoffs. It should be interesting to see how the models from the rest of the group performed.

This week, Dr. Posta tasked me with an additional model that only takes the results of the last two games of the regular season for each of the 12 playoff teams in the first round. It seems like a very small sample size, but it should be interesting to see if only using the results of the last two games might best represent a team's current form heading into the playoffs. Particularly for teams that might have gotten off to a slow start but came into form late in the season. We’ll see how this model pans out.

Additionally, this upcoming week we are also going to be calculating both the binomial deviance and squared error for our predictions of the first round, in order to help us determine what the best model will be for us to use as we aim to predict this year's playoffs.

 

 


Comments

Post a Comment

Popular posts from this blog

Fall 23: Week 1

Image
      Coming into this semester, I really wasn’t sure what I would be doing for my authentic experience. I ended up with a few different exciting prospects including a coding project with Professor Manning and a Calculus project revolving around the tabular method for integration. However, at the last minute I came across a Sports Analytic Research Project being led by Dr. Posta here at PC. As a huge sports fan, I could not have dreamed up a better research project than one marrying math and sports. I was fortunate enough to be chosen to participate in this internship and I am beyond excited to be a part of this project!
Read more

Week 4

Image
      This past week, Dr. Posta tasked me with again building a simple model to predict an upcoming match between the Phoenix Rising and Detroit City FC. For this week, there was a focus on using Excel formulas while building this model. Once again, using stats from the past 10 games, I calculated expected goals for both teams heading into this match. Using that xG stat, I calculated the probabilities of several different possible outcomes of the game. Ultimately, the model expected the Phoenix Rising to win 29% of the time, lose 26% of the time, and draw the remaining 46 percent of the time, with the single most probable outcome being a 1-1 draw 12% of the time. The actual outcome of the match was a 5-0 blowout by the Phoenix Rising. The model only expects a 5-0 outcome 0.00006 percent of the time, so the result of this match was very much a statistical anomaly. This is what brings us to the next stage of our research project, data collection. For this project, we are going to
Read more

Week 2

Image
  A bit late with my blog post for this week. We got hit pretty hard by the storm on Thursday and lost both power and internet. Thankfully power was restored early Friday morning but four days later, we are still without internet service. Have a technician coming out tomorrow, so hopefully that gets resolved. Not having internet access while taking online classes is problematic to say the least. But I digress. Earlier this week I had the pleasure of meeting with Dr. Posta. We discussed what the internship will entail in greater detail he got me started with some further readings on the model we will be using for this project. Soccer is generally a low-scoring sport. This can be inherently problematic when attempting to create a model that can accurately predict match outcomes. Interestingly, the model doesn’t place as much weight on match scores as one might initially think. Take for example a match where team A defeats team B 2-0. In this match, team A has 2 shots on goal and sc
Read more