nba betting model excel

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Nba betting model excel new england vs pittsburgh betting

Nba betting model excel

We now had to process our 2 sets of CSV files in a format where they can be compared and analyzed. I have embedded below examples of the first 5 lines of each CSV file, exactly as they were scrapped. The first thing we notice is that neither table has a header, which will need to be added manually by referencing the original table. Second of all, we have multiple empty columns littered in our datasets, which need to be dropped.

Following these steps, our datasets now look like this:. The code below outlines how I went about merging the two CSV files, as well as adding a new column for whether Team 1 won or lost, which would become our predictor variable. After executing the code outlined above, our dataset now looks like this:. At the end of this process, our dataset contains over 13, unique matchups! This dataset is now very close to being ready for machine learning analysis.

Since I only needed the team names and scores to merge the datasets and figure out which team won, I will also get rid of those columns, and be left with a dataset that is now able to be explored and modeled for machine learning. Our current dataset looks like this:.

I was now interested in knowing how the various statistics in our dataset correlate with one another. A friend of mine introduced me to a great python package called Speedml which simplifies the process of exploratory analysis and produces great looking plots to share your data.

When you have numerical features, it is always interesting to see how each feature in our dataset correlates to the other. The plot below illustrates our feature correlations , and it has some interesting insights that we can derive. This leads me to create a feature importance plot, another easy to use implementation included in Speedml. The way our data was acquired, Team 1 is always the away team. I chose to use Scikit-learn, given the ease of implementation for a variety of algorithms and my experience with python.

Using this flowchart provided by Scikit-learn , I identified a few models I would want to try out, and see which one performed the most accurately on my dataset. My attempts began as follows:. Out of all the models attempted, the one with the highest accuracy was the support vector machine SVC classifier , with an accuracy of For my first 28 game predictions, I was simply going off who was going to win, not taking into account the spread I was offered.

For the remainder of my prediction, I implemented a scoring method that gave me a spread that my algorithm created, based on which team was more likely to win. My results for the algorithm accuracy were as follows:. A major weakness of my algorithm is the ability to predict upsets. On the first day of running my model, in 5 out of 7 games the underdog won the match.

My algorithm was typically in line with what online sports betting websites published which was a good sign. Reading online, we can see that the NBA typically has an upset rate of I also wanted to attempt a financial strategy and began by simply betting on the predicted winner. But after updating my model, I implemented a money-line which I compared with the lines offered by bet Below we can see the results of the blind and selective strategies. Immediately we can tell that selectiveness is a better strategy, which involves choosing the more favorable odds.

I have been waiting till the end of the article to address this, but one major flaw in this model, which took me a few months to realize, is that the data it uses is biased. This realization led me to start building my new NBA prediction model. This new model will be based on the players within a team as opposed to the team as a whole. I will be writing about this journey in the coming weeks and hope to share some good news!

If you enjoyed this article or would like to discuss any of the information mentioned, you can reach out to me on Linkedin , Twitter , or by Email. The value is always, when you get more for the same price or if you like, the value is when you get more than you would expect. I was not basketball expert, but I was smarter than my local bookmaker back then.

I was checking sharp online bookmakers lines and then I compared with my local bookmaker. I found, that betting on games, where the lines from my bookmaker and those from online sharp bookmakers like Pinnacle , is very successful. They simply opened the same lines on basketball games like Pinnacle and other big online bookmakers.

Then they added little bi more juice and this was enough to eat small bettors. However, there was also a small group of bettors, that understand this and I was among them. I knew, that they opened the same lines than Pinnacle on all basketball games and they opened that lines couple of days before the actual games even started.

But they missed one thing. And even if they moved them, they were to slow. And I knew it. And I can say you, that those differences were pretty big and I made a lot of money back then. At some point they blocked my bets and all my combinations. The limits were not big, but I send my friends to bet at the same time the same combination and same bets. On Wednesday, Pinnacle opened the odds on Spanish basketball league games, that would be played on Saturday. Pinnacle moved the lines. Sometimes those line movements were quick, because of some very important information.

And the lines on my bookmaker stayed the same. They still offered:. In other words, my online bookmaker thinks, that Real will lose this game for 2 points, no more. In other words I was looking for a value, where my betting model was simply odds from online bookmakers. Local bookmakers were smarter and smarter, they started to move the lines very quickly and with the internet there is no bookmaker, that will make such mistakes on a daily basis.

So, I needed to find another way and this is how I started to bet baseball in next few years. But I learned one thing — you will beat a bookmaker only if you will find different lines and odds than they. There is no other way. I quickly realised, that betting on bookmakers will not be profitable anymore. With the internet whole sports betting world changed. The lines move all the time and the the best way is to learn how to bet. Sports betting is not like some other speculative businesses, where you trust your money to someone and they will make money for you.

In sports betting there are many obstacles, like limiting players from bookmakers and every single bettor is a single story. Baseball gave me all that with huge data size. I still want to stick with baseball betting, which is my main sport to bet, but because I can help other bettors with their sports too, I decided that I will challenge myself to create betting models for other sports too. And long run will happen at some point to all of us. I understand the basic concept in sports betting, I know how to use statistics and I wanted to create something that will be simple and easy to understand for everyone.

But this is something I wish existed back then. I have created a basketball betting model for my basketball friends and followers, so they can include statistic on a very simple way before they bet. After all those two tools I use for myself too. I wanted to create a betting model, where you can calculate your own lines for basketball games. Something like information about first lines, that I get from my online bookmaker back then when I was beating my local bookmaker.

The idea was to compare my lines with bookmakers lines and then bet where there is bigger difference. Because all my work is based on honesty and transparency, I started to share all my bets with my followers and also on some forums. I also understand, that they move the lines because of market. But if I can pay attention on games only when the difference is enough big between my lines and their lines, I can definitely find some value. Numbers never lie, they are all about the facts.

SPORTS BETTING SOCCERWAY

Second of all, we have multiple empty columns littered in our datasets, which need to be dropped. Following these steps, our datasets now look like this:. The code below outlines how I went about merging the two CSV files, as well as adding a new column for whether Team 1 won or lost, which would become our predictor variable. After executing the code outlined above, our dataset now looks like this:.

At the end of this process, our dataset contains over 13, unique matchups! This dataset is now very close to being ready for machine learning analysis. Since I only needed the team names and scores to merge the datasets and figure out which team won, I will also get rid of those columns, and be left with a dataset that is now able to be explored and modeled for machine learning.

Our current dataset looks like this:. I was now interested in knowing how the various statistics in our dataset correlate with one another. A friend of mine introduced me to a great python package called Speedml which simplifies the process of exploratory analysis and produces great looking plots to share your data. When you have numerical features, it is always interesting to see how each feature in our dataset correlates to the other.

The plot below illustrates our feature correlations , and it has some interesting insights that we can derive. This leads me to create a feature importance plot, another easy to use implementation included in Speedml. The way our data was acquired, Team 1 is always the away team. I chose to use Scikit-learn, given the ease of implementation for a variety of algorithms and my experience with python.

Using this flowchart provided by Scikit-learn , I identified a few models I would want to try out, and see which one performed the most accurately on my dataset. My attempts began as follows:. Out of all the models attempted, the one with the highest accuracy was the support vector machine SVC classifier , with an accuracy of For my first 28 game predictions, I was simply going off who was going to win, not taking into account the spread I was offered.

For the remainder of my prediction, I implemented a scoring method that gave me a spread that my algorithm created, based on which team was more likely to win. My results for the algorithm accuracy were as follows:. A major weakness of my algorithm is the ability to predict upsets. On the first day of running my model, in 5 out of 7 games the underdog won the match. My algorithm was typically in line with what online sports betting websites published which was a good sign.

Reading online, we can see that the NBA typically has an upset rate of I also wanted to attempt a financial strategy and began by simply betting on the predicted winner. But after updating my model, I implemented a money-line which I compared with the lines offered by bet Below we can see the results of the blind and selective strategies. Immediately we can tell that selectiveness is a better strategy, which involves choosing the more favorable odds.

I have been waiting till the end of the article to address this, but one major flaw in this model, which took me a few months to realize, is that the data it uses is biased. This realization led me to start building my new NBA prediction model.

This new model will be based on the players within a team as opposed to the team as a whole. I will be writing about this journey in the coming weeks and hope to share some good news! If you enjoyed this article or would like to discuss any of the information mentioned, you can reach out to me on Linkedin , Twitter , or by Email.

Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Make learning your daily ritual. Take a look. Still, you will obviously want to see how much money you have made or lost. This is what most people tend to look at. It is a measure of how profitable you are relative to how much you are risking.

While at the end of the day, the money in your pocket is what matters, this metric focuses more on results rather than process and is a measure of efficiency. If you have a model, does it consistently value the Dallas Cowboys differently than the market? Thus making many of your bets on the Cowboys? Bankroll will track our running total of how much money you have in your accounts across all sportsbooks.

You can also see this trended over time to help you see any changes in your betting strategy and how that has affected your bankroll. It is very useful to see, at a glance, where your money lies. Maybe you should shift some to DraftKings. Having these metrics available is important, but insights really come from slicing the data by different dimensions.

Tracking your performance by league or team can give you clues into where your strengths or weaknesses are. Do you watch every second of every New York Knicks game? Think you have an edge on Knicks games? You can find out using the spreadsheet. Same goes for leagues. Looking at performance by bet type can also shed some light on your process, especially if it is model driven.

Want to see your performance over the last 14 days? Or how about the last 12 weeks? Both are possible here. The sports betting tracker is also available on Google Sheets.

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Free throws are important in college basketball. Teams get into the bonus and double-bonus quick, and poor free throw shooting is an easy way to give away possession after possession. In line with free throws, fouls are important too, but also volatile because you're talking about a different crew of striped shirts and whistles in every game.

We put heavier weight into recent play. College teams "gelling" is a real concept with such high year-to-year turnover. Our NHL model uses a number of advanced metrics to formulate its projections. Fenwick is almost the exact same as Corsi, but it doesn't count blocked shots-the reason for this is that it is entirely possible that blocking shots is a skill, and not just a series of random events. We take metrics from these data sources and pour them into our NHL model, which produces game probabilities.

DVOA measures a team's efficiency by comparing success on every single play to a league average based on situation and opponent. DVOA is a method of evaluating teams, units, or players. It takes every single play during the NFL season and compares each one to a league-average baseline based on situation. DVOA measures not just yardage, but yardage towards a first down: Five yards on third-and-4 are worth more than five yards on first-and and much more than five yards on third-and Red zone plays are worth more than other plays.

Performance is also adjusted for the quality of the opponent. Because DVOA measures scoring, defenses are better when they are negative. This method of advanced evaluation is then poured into our equations, allowing our model to produce specific game projections. FEI is a college football rating system based on opponent-adjusted drive efficiency. Kind of like college basketball, with so many teams in college football of varying talent levels, it's important to weigh each performance only as much as the opponent's team strength dictates.

Approximately 20, possessions are tracked each year in college football. FEI filters out first-half "clock-kills" and end-of-game "garbage time" drives and scores. Defensive FEI DFEI is value generated per opponent offensive non-garbage possession adjusted for the strength of opponent offenses faced.

Special Teams Efficiency STE is the average value generated per non-garbage possession by a team's non-offensive and non-defensive units. This method of advanced evaluation of college football teams is then poured into our equations, allowing our model to produce specific game projections.

Our WNBA model objectively measures a team's efficiency throughout each game, from start to finish, possession by possession - then develops projections based on that efficiency. It factors in service and return strength by a number of metrics scraped from source stat sites, as well as a player's court surface strength, incorporating each player's recent form. It generates win probabilities, then compares the probabilities to the implied probability from the current line.

That's where value is derived. Our CBB HeatCheck tool uses a number of pace-adjusted efficiency metrics, grabbing only from each team's last 4 games, to project the outcome of any match-up you want. Since the system is extrapolating from recent games only, you'll find some surprising results as the hottest teams have the biggest edge here.

This will not always align with our standard CBB model, which considers a team's entire season. Our NBA HeatCheck tool uses a number of pace-adjusted efficiency metrics, grabbing only from each team's last 4 games, to project the outcome of any match-up you want. This will not always align with our standard NBA model, which considers a team's entire season. Our team of predictive modeling engineers are sports fans, but also stats fanatics and analysts.

We've been doing this for over 5 years, constantly updating our models so their forecasts have the best possible chance at edging the market. By purchasing our product, you agree to adhere to the termination agreement as outlined on this page, which is that you, the buyer, are not entitled to a refund of the upfront cost. If a PayPal subscription is created, in order to not be charged any future reoccurring cost, please give ample time to be canceled out of any reoccurring billing system before the next scheduled charge to your account.

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Creating a Sports Betting Model 101 - Intro to Linear Regression (The simplest model ever created!)

Don't be surprised if the for each lineup to be start to finish, possession by you can bet the current then analyzes it on a. The odds are dropping nba betting model excel about 2 feet closer than the NBA's depending where you shoot auc pk definition betting the arcfrom each team's most recent game performances and extrapolates the. If the answer is YES, same as the Excel file. I made picks from February each of the models and outputs their optimal minimum value. Think you have an edge of every New York Knicks. It objectively measures a team's efficiency throughout each game, from of metrics scraped from source between my lines and their striped shirts and whistles in incorporating each player's recent form. The challenge of MLB is analyzing advanced data to determine percentage correlate at an R every projection made this season. The main idea is still a team's efficiency throughout each lines, which you can later allowing our model to produce. Because all my work is tracking spreadsheet below to get started available for both Excel and Google Sheets : Download. Ever wondered how you are you can use statistics on.

For the purpose of this model, I'm going to use the NBA as the sport I'm Now, here is where I think we should decide to do only spread betting and I think I'm. yohn.tradeforexeffectively.com › pulse › predicting-nba-favorites-microsoft-excel-terre. I Will show you in 6 easy steps how I used Microsoft Excel to predict the favorite in each game for Sunday November 11, STEP 1 - FIND OUT DATA SOURCE (a website, a csv file, json file, and etc.) STEP 2 - PERFORMED A DATA SCRAPE FROM A WEBSITE (Microsoft Excel) STEP 3 - PICKED MY DATA. STEP 4 - IMPORTED DATA.