The main usefulness of this API is to find team sentiment which ultimately may move a line before a game starts. This is no way intended as a predictor of the result of a game, but rather whether a bet should be placed now or later depending on the direction of sentiment. Browse their Python and NodeJS code samples to get started. Check out this NFL Arrest API, which provides updated data on which players and teams have been arrested or commited a crime in recent times. The AllSportDB.

### Using ESPN's new Fantasy API (v3)

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Tensorflow sobel edges exampleThere are over a dozen sportsbooks currently integrated with many more to come, including: 5Dimes Pinnacle includes in-play! Matchbook includes in-play! Popularity Score: 9. Popularity Score: 8. Ever wondered which crimes certain NFL players have committed? Search by crime, teams, players, positions and more. Popularity Score: 6. Search events by league, country, and more.

Using the API you can get sports and calendar data. Preview their NFL data here. NFL Season is returning soon. Search this website. Comments Very useful article…. Leave a Reply Your email address will not be published. About Team Jobs Contact Us. Connect to API.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here.

Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I've built a simple script that chooses a number from a list of set lottery numbers pre-chosen by our fantasy football commissioner. Per the results of this years fantasy football results, each team depending on how well or poor they did in the regular season receives a designated number of chances to pick a lottery number. The problem is that the script I've written chooses all numbers for one team before it moves onto the next team.

I would like the script to pick numbers one at a time moving down the list of teams. There is an added level of complexity because not all teams have the same number of chances of picking a lottery number. So I'm using counters for each team which decrements down to zero, which would mean they have chosen all the numbers allotted to them.

I'm not sure how to make this script pick numbers one at a time. Mathematically there's no difference between assigning all the numbers for a give team first, or going round robin.

At least, that's true as long as your numbers are being selected at random, which it seems they are if inefficiently. So you don't technically need to change anything. But if you want to change things anyway, there are much more elegant ways you can go about it. For one thing, using separate variables for each person in your league is very awkward you should use a list!

A big performance improvement would be to shuffle the list of numbers just once, then take values from it in order rather than selecting random items and needing to delete them from the middle of the list.

**5 Must Have Skills To Become Machine Learning Engineer**

Here's my quick minimally tested attempt at solving this, using the roundrobin recipe from the itertools documentation :. Round-robin isn't necessary for a random selection. You really should think of factoring out your common code. In addition, you might want to consider a different data structure - namely a dict :. Learn more. How to make my Fantasy Football lottery script more Pythonic? Ask Question. Asked 3 years, 3 months ago. Active 3 years, 3 months ago. Viewed times.Note: We started a new blog where I will be running the updated algorithm and posting updates on how my team is doing throughout the 19—20 season here:.

Our research started by pulling the most up-to-date player stats from the Fantasy Game API and running statistical analysis on all the EPL teams and all individual players using Python. Key questions our analysis aims to answer:. Note: This project was executed on Nov 14th,after Gameweek 10, so all the data and tables presented in the article are accurate up to that date. We can clearly see that in general there is a linear correlation between how well a team is doing in the English Premier League and the cumulative fantasy points of its players.

This also exposes a few of the teams that would be considered a bad investment such as — Tottenham, Arsenal, Manchester United, Fulham, Huddersfield, West Ham, and Southampton. This will help us identify the teams that have too many expensive and under-performing players who rarely play the full 90 minutes each game due to the frequent squad rotation their coach employs, which makes them a bad investment in the long run, since they will not be generating fantasy points consistently each game.

Also, this graph below will help us identify the teams with coaches that do not use frequent player rotation, which results in those teams having a more consistent core of regular squad players. That will inform our algorithm to pick more players from those teams since their players are expected to generate a higher aggregate ROI in the long run because they would be involved in a lot more game action on average compared to players from teams that use frequent player rotation.

That means that most of Wolverhampton's players are undervalued compared to their performance and that the coach uses the same 11 players on a regular basis and only uses subs towards the end of the game or when a regular team player gets injured.

Even teams like Manchester City, Liverpool and Chelsea are in this category with 13—14 regular squad players, which means that picking players from any of the teams above is a good investment in the long run because the regular squad players play more minutes on average compared to players on the bench. After identifying which teams yield a higher cumulative ROI, we then zoomed in on the individual players.

In stock market terms, we have identified all the high-yield market sectors — the teams — and now we want to start analyzing all the individual stocks in each sector — the players. The plan is to isolate a list of players with the highest ROIs and write a Python algorithm that will use smart logic to pick the most optimal combination of players, which will generate the highest return on investment for our limited budget of MM.

Looking at the scatter plot of Player Cost vs. Player Total Fantasy Points above, we would want our AI to pick players who appear as west-north as possible on the plot players of low cost who generate a lot of Fantasy Points. Note that we would also want to include some of the top players from the east-north corner of the plot since these would be some of the star league players who generate a lot of points, and even though they are a bit expensive, they still end up with a decent ROI.

The graph below plots the Top 20 ROI vs. In the Pie Charts below, we can see a distribution of the teams with the most-overpriced players versus the teams with the most undervalued players. We are expecting our final algorithm to pick players from a variety of teams that have a lot of high-yield players such as — Bournemouth, Wolves, Liverpool Chelsea, Manchester City, Watford, and Everton.

To understand the logic of our Algorithm one must first understand the rules and constraints of the EPL Fantasy Game below:. So, we started our python algorithm with an if-else statement for these conditions and then added our own conditions and logic on top of that, so that each time that the algorithm loops through our list of players, it can use smart logic to make a valid pick guided by our conditions below:.

Here is some of our condensed Python Code for the team picking algorithm:. Below you can see a screenshot of the final team that our Algo picked:. Note: The team below is only accurate up to Nov 14th, Note: We wrote a similar algorithm for the AVG Joe team, which focuses more on spending the budget on star players from big teams, who are often overpriced and might not return the highest cumulative ROI for our limited budget of M.

Note: We also asked a classmate to pick a random team of his own, so we can compare his picks and verify that our random team-selector function for the AVG Joe algorithm is accurate.

The final results showed that our team scored a total of points vs. The bar-plot below demonstrates our results:. Did it beat the others by a significant margin? Did our Algorithm successfully pick players from some middle of the table teams, which we initially identified as undervalued?

Modelling credit cyclesDid the AVG Joe Algorithm and our classmate pick more of the expensive overpriced players from the top teams? Below we can see that our Algorithm picked a combination of players from most of the high ROI teams that we identified at the beginning of our project:.Welcome to part 5 of the Python for Fantasy Football series! This article will be the first of several posts on machine learning, where I will use expected goals as an example to show you how to create your own machine learning models in Python from start to finish.

Advmenu plusAs always, you can follow along with this post by downloading the notebook from my GitHub account. Imagine you wanted to calculate the net force on an object based on its mass and acceleration. This is a task that you could easily carry out yourself with a basic calculator.

Imagine instead trying to predict the price of a house based on the number of rooms, location, age, size, outside space etc. In this approach, a human provides the algorithm with examples of the target output it expects, for instance the actual selling price of a house.

Once trained, a good model will be able to come up with useful predictions about previously unseen data. It makes sense to use supervised learning for expected goals models as we can train the model by feeding it information about whether a shot actually resulted in a goal or not. As the name suggests, in this approach the model is given unlabelled data and is left to find correlations between the inputs on its own.

Nds rectangular riser for valve boxA common use of this type of learning is to group people based on their purchasing trends to help identify things they might be interested in buying in the future. The machine is given a reward function and it trains itself using trial and error to maximise the reward. Regardless of the method used, the steps for creating good machine learning models are as follows:.

The reasons for this are as follows:. Before exploring the data, we need to import the modules we will use throughout the rest of the article. We can get all of the matches from these two competitions:. Next, we need to pull all the shot events from the data. One thing worth doing though is to remove penalties from the data, as they are a unique situation that could cause the model to over-predict open-play goals from the same distance as the penalty spot.

We will see later how an imbalance in our target variables can lead us to be more confident in our models that we should be. Supervised machine learning problems can be split into two broad categories — classification and regression.

A regression problem is one where the values you are predicting are continuous, for example the price of a car. In our case we are trying to predict the probability that a shot will result in a goal, so our xG output values can be any number in the rangewhich makes this look like a regression problem at first glance. After a brief review of the most popular public xG models see here for a listas far as I can tell most models are using either linear or logistic regression, with Martin Eastwood trying out a Support Vector Machines model link.

You will often see people quoting R2 values of 0. RMSE essentially measures how concentrated the data is around a line of best fit, i. This can be a useful statistic to evaluate and compare different regression models, but despite the confusing name logistic regression is used for classification problems, not regression!

A lower RMSE value does not imply a lower error rate in classification models, as explained here. So what metrics should we use?

La purga di bebéIn our case that model would have an accuracy of Instead, it makes sense to consider the absolute values of correct and incorrect predictions for each class.

These predictions can fall into four separate categories:. His logistic regression model achieved the following results:. Ideally we want the model to achieve a high precision and recall for the positive class, but there is often a trade-off between the two, so it makes sense to aim for a high recall here as a minimum requirement.

The reason for perhaps unfairly, sorry! That said, we might as well try to overcome the limitations of the data as best we can by addressing the class imbalance, choosing a suitable classifier and tuning the parameters. These tasks will be the subject of future articles, but for now we will get started with a basic model.

Since neural networks typically only outperform other algorithms when dealing with very large datasets, I will instead focus on tree-based models throughout this series, which are widely used and considered to be among the best models for classification problems. Unlike logistic regression, tree-based models handle categorical features, correlation and non-linearity well, which is important since most of our model inputs will be categorical variables that can have some correlation to one another.Released: Feb 15, View statistics for this project via Libraries.

The documentation are hosted at readthedocs. Feb 15, Feb 3, Jan 28, Jan 27, Dec 15, Dec 14, Dec 9, Dec 1, Nov 4, Nov 3, Oct 9, Oct 6, Oct 3, Oct 2, Sep 29, Sep 28, Sep 8, Sep 4, Aug 28, Aug 26, Jul 9, Jul 7, Daily Fantasy Sports lineup optimzer for all popular daily fantasy sports sites. Also includes source code for the ESPN Fantasy Explorer, an interactive visualization tool for public fantasy football leagues. Running some simple linear regressions to determine the importance of the O-line in QB and RB fantasy performance.

Python script to enumerate all possible playoff scenarios. Only useful in the last few weeks. Repository that contains useful functions of Fantasy Premier League. This project scrapes the FPL site for data and creates csv files for use in Tableau. A project that graphs each team's points vs. Add a description, image, and links to the fantasy-football topic page so that developers can more easily learn about it.

Curate this topic. To associate your repository with the fantasy-football topic, visit your repo's landing page and select "manage topics. Learn more. Skip to content. Here are 28 public repositories matching this topic Language: Python Filter by language. Sort options. Star Code Issues Pull requests. Updated Apr 14, Python. Updated Mar 1, Python. Updated Oct 17, Python. Updated Feb 2, Python. Updated Jan 13, Python. Automate playing Yahoo Fantasy Football. Updated Mar 17, Python. Updated Sep 10, Python.

## Learn Coding & Data Science Fundamentals with Fantasy Football

Star 7. Star 4. Daily fantasy sports lineup optimization package for Python. Updated Apr 29, Python. Star 3. Premier League Fantasy. Updated Oct 31, Python. Updated Feb 15, Python. Star 2. Updated Nov 22, Python. Fantasy Football Toolkit.Regular readers will be aware that I am a big advocate of using data to help better understand sports, and daily fantasy football lends itself particularly well to this type of analysis.

Many of you are probably already familiar with spreadsheet software like Excel, and whilst that is very powerful it often lacks the flexibility and functionality that you can achieve by writing your own code. Now that you want to learn to code I hope! The most useful discipline for sports analytics is data science, with the most popular languages being Python and R. I have used both and personally prefer Python, so I will be focusing on that throughout this series.

To do so, I recommend downloading Anaconda for Python 3which already comes pre-loaded with the most popular data science libraries and tools you will need. You can find a step-by-step guide on how to install Anaconda on Windows here and Mac hereas well as in the Anaconda documentation itself here.

Once installed, open up a Jupyter notebook either via the shortcut created during the Anaconda installation process, or by typing jupyter notebook into the command prompt. Jupyter notebooks provide a nice environment to allow you to write, test and edit code.

You can either create a new notebook file to follow along with this article, or download the one I made earlier via Github. Keep in mind that a huge part of being a good programmer is just learning how to adapt existing code to your particular needs, so if you get stuck at any point just Google it!

The vast majority of the time you will find out that someone has already asked the same question on stackoverflow.

Getting data in the first place can be tricky at the best of times, so I have provided a csv file containing the data that can be downloaded from my Github account here. This exercise will teach you the basics of the pandas library for data-frames, as well as some basic plotting with matplotlib and seaborn.

First, we need to import the pandas library and load the data. With a small dataset like this we can see every row pretty easily, but with larger datasets you are likely to want to use the head and tail methods to get an idea of what the data looks like:. Manchester City and Liverpool unsurprisingly lead the way so far, with Wolves and Bournemouth keeping pace with Chelsea and Spurs in the early going.

The teams at the bottom of this dataframe have been poor, but there are a couple of teams in the middle that stand out for different reasons. In this case we will create a horizontal bar plot barh using matplotlib to look at goal difference vs expected goal difference:. The plot shows that Arsenal have been significantly over-performing in terms of xGD, whereas Cardiff have been under-performing. Fortunately there is a different plotting library, seaborn, which allows us to easily create much more aesthetically pleasing plots.

Much better! Feel free to continue playing around with the data on your own to see what other interesting bits of information you can find e. We barely scratched the surface in this article, but hopefully you are starting to get an idea of the Python syntax, as well as a sense of how powerful it can be. I wanted to keep this fairly brief, but if you have time I highly recommend taking a free introductory course to get even more familiar with the basics, for example the one by DataCamp here.

If you enjoyed this article, please share it on social media and be sure to look out for the next part! This is brilliant stuff. Hope this sparks some interest for others to get going. Keep up the good stuff. Just one small advice: You can make his elaborate from here on. If someone has come this far, they probably would want to dive deep and see some decent stuff which even they can create.

Cheers man! Hello, I am creating a fantasy football program for my final year project at university. I wanted some information regarding how you would go about picking the best players for your fantasy football team based on last years data and how you would start this project and what software would be used. Any help i appreciated. There are a few ways to go about this.

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