DEV Community: Kmulla19

Do Fantasy Stars Equal Wins Pt. 2

Kmulla19 — Mon, 15 Aug 2022 14:28:16 +0000

(https://github.com/Kmulla19/Blog/tree/main/Blog3)

In my last post, we began looking at the relationship between the number of Top 100 Fantasy PPR on a team, and that team's total wins.

There were some interesting findings, with a basic Linear Regression modeling explaining 40% of the variance in that team's wins.

So what's the other 60%? What else could potentially further explain the number of wins an NFL team has in a season?

Obviously, there are a number of factors that determine the outcome of each individual game and hence, the total number of wins a team has. What about consistency among those fantasy stars week in and week out?

It's tough to be consistent in the NFL. If you're a Running Back, you can't help it if a star member of your Offensive Line is out one week. You also don't decide who you play each week. The NFL decides who you play based upon where you finished the previous season. Basically, the teams that did well, will play more of the other teams that did well in the following season.

But it's still worth looking into if there is a connection between how consistent a team's Top Fantasy players are in terms of point variance, and how many wins that team had. When I say point variance I mean does the player put up roughly the same numbers each week or do they have a lot of boom and bust weeks that average out to a good points per game average.

First, we need to determine each players points per game. This is easy. We simply take the total number of PPR points they had on the season, and divide it by the number of games they played. Remember not every player, plays all 17 games.

Next, we need to look at how each player did on a weekly basis. Now Pro Football Reference did not have the weekly PPR points for each player on the website but they did have the formula for how PPR is calculated and each player's stats for each week.

Okay that's a little annoying but nothing some Python can't solve. First we need to pull in the weekly stats for each player and then have the stats go through our PPR formula to determine the number of points they had each week. This can be done by using a web-scrape similar to how we pulled in the top players and team standings. We created a for loop that takes the player's name in our Top 100 PPR DataFrame, then pull's in there weekly stats, and puts them through our custom PPR formula.

We tested a few players season total PPR points and found that our formula is usually off by about 1 point in either direction which we are fine with summing up with rounding error. I don't expect this to skew the data much since we are talking about hundreds of total points so it changes the data by less than 1%.

Now that we have each player's weekly stats we can look at the standard deviation in points as a percentage, relative to their weekly average. We need to look at it as a percentage because a 5 point std for a player who averages 20 points per week is different than a 5 point std for a player who averages 12 points.

Stay tuned for the next blog post where we will look at these variance numbers per player, team, and how that affects wins.

Do Fantasy Stars Equal Wins

Kmulla19 — Sat, 06 Aug 2022 18:20:32 +0000

(https://github.com/Kmulla19/Blog/tree/main/Blog2)

How much of an impact do high performing offensive fantasy players make on their team winning?

It's easy to quickly conclude that the top NFL teams would have more fantasy players right? After all, you get points for doing things that are good for your offense.

But what if the defense scores a touchdown? That helps the team win but harms their offensive fantasy players as the other team now has the ball again. In addition, usually when a team is behind, they are forced to pass in an attempt to score quicker. This can lead to some big fantasy numbers for the quarterback of the team that ultimately loses the game.

Well today we are going to explore the correlation of the number of wins a team has, and the number of players that team has in the top 100 PPR ranks for that season (PPR is a type of fantasy football point counting style). We will also begin building a simple linear regression model that we can we expand onto in the future.

First we need to get two different sources of data. One is the NFL standings for last season. The second is the top fantasy players for the NFL last year.

Now we need to filter down to the top 100 PPR players, see which team they are on, and compare the count of top players on each team, with the number of wins they had.

The correlation between these two variables is .66, which is pretty strong.

A simple linear regression model between these two variables is .42(.40 adjusted). This means that the number of players in the top 100 PPR for each team explains 40% of the variance in that teams wins.

Now what else could we explore to better understand the connection between these two?

We could look at the total fantasy points scored by each team's players in the top 100. Two teams that each have 3 players in the top 100 could have drastically different total fantasy points scored by those 3 players.

Also, how does the position of your top fantasy players affect the win total? As I mentioned earlier, having a quarterback with a lot of fantasy points isn't always a good thing. Are top performing running backs a sign that your team was in control and didn't need to pass as much?

It's interesting to note that the top fantasy player in the league last year just so happened to be on the Super Bowl winning Rams. However, the Rams only had 12 regular season wins while two other teams had 13 wins.

Here are some other things I noticed or want to further explore:

Every team with 10 wins had at least 3 top fantasy players
The bottom two teams had 3 fantasy wins, but the next 5 had 1 or less.
"Points For", a team's total points scored throughout the season has a 87% correlation with win %.

Analyzing Trends in Basketball. Vol 1

Kmulla19 — Tue, 05 Jul 2022 03:12:09 +0000

Each year, millions of teenage boys play basketball for their high school. From there, those who are good enough, go on to play and college. It's only the truly exceptional, that make it to the NBA. In fact, less than 4,500 men have ever played professional basketball in its 77 year history. But what's ever more fascinating is how the game has evolved as much in the last 7 years as it did in the previous 70.

The year is 1982, and the NBA Leader in 3 pointers is Don Buse of the Indiana Pacers. He scored a whopping 73 in 82 total games. For comparison, Steph Curry made 285 in only 64 games in the 2021-2022 NBA season. So what happened? Why did the NBA Leader in 3 pointers per game go from less than 1 per game to 4.5.

In this series, I'll be discussing how data analytics, and a few special players changed the game. We'll be taking a look at some basic formulas/code to explain the rules of the game and how to optimize your chances of winning from the eyes of a data scientist.

So what is a 3 point shot? Simply put, the 3 point shot is a shot attempt beyond the 3 point line on a basketball court. The idea for it is that because it is a more difficult shot than a traditional 2 point shot, it should be worth more if you make it. There are other shots called free throws that are worth 1 point per attempt, but for now, let's focus on the comparison of the 2 and 3 point shot.

Much of the credit for the recent evolution of basketball to beyond the 3-point line is credited to Steph Curry but this is a trend that has been happening gradually since its adoption by the NVA in the 1979-80 season. 10 years ago the NBA 3 point leader was Ryan Andersen of the Orlando Magic with 166 made in 61 games or 2.7 per game. It seems so simple right?

As this series continues we will further examine the data to see if there is a correlation between 3 pointers attempted/made and wins? In order to do that we will have to determine the values of the winner's relative to that of the opponents and compare this across random samples from both recent and passed years.

If there is a correlation, that could explain why NBA teams are shooting and making so many more 3 pointers each year. But what, if anything, would cause them to revert back to playing focusing more on the 2 point shot? We will examine at what percentage a player would need to make their 2 point shots and free throws in order to revert back from the current 3 point trend.

Credit all stats to landofbasketball.com