cricket betting algorithmExploring the Impact of Innovation and AI on the Sports Betting Industry
In the digital age, technology has become an integral part of our lives. It has revolutionized almost every industry in one way or another – and the sports betting industry is no exception. As a matter of fact, thanks to the growing impact of innovation and Artificial Intelligence (AI), this particular sector has undergone a drastic transformation over the last few years.
Innovation has enabled players to get more involved with their bets than ever before. For instance, they can now access various data-driven tools that provide them with detailed insights into the teams and players they are betting on. Moreover, AI-powered algorithms have allowed for automated bet tracking systems which help users make smarter decisions when placing their wagers.
Additionally, AI-based prediction models enable users to predict future outcomes with greater accuracy than ever before. With this technology and a little help form a betfair promotion code, players can maximize their chances of success when it comes to sports betting.
Artificial Intelligence (AI) has had a major impact on the sports betting industry. AI-powered algorithms are now being used to analyze data and make predictions about the outcome of sporting events. This technology is helping bookmakers to better understand the odds and make more accurate predictions, which can lead to higher profits for them, as well as better outcomes for their customers. By using AI technology, bookmakers can create more accurate betting models, which can be used to determine the best times to place a bet and how much money should be wagered on each event.
AI is also being used to create automated betting systems that can place bets on behalf of users without any human intervention. These systems use machine learning algorithms to identify patterns in past results and then use this information to predict future outcomes with greater accuracy than traditional methods. Additionally, AI is being used by bookmakers to detect suspicious activity and prevent fraud, as well as monitor customer behavior in order to provide personalized services.
All of these applications are helping the sports betting industry become more efficient and profitable. AI is making it easier for bookmakers to make more accurate predictions, detect fraud, and provide better customer service.
Machine learning has had a huge impact on sports betting odds. By using machine learning algorithms, bookmakers can now accurately predict the outcome of sporting events and set more accurate betting odds. This means that bettors have access to more reliable information when placing their bets, which can help them make better decisions and increase their chances of winning.
Machine learning also allows bookmakers to quickly adjust their odds in response to changing conditions or new information, such as injuries or weather forecasts. This helps ensure that the odds remain fair and balanced for all bettors. Additionally, machine learning algorithms can be used to detect suspicious betting patterns and alert bookmakers to potential fraud or manipulation attempts.
All in all, machine learning has revolutionized the way sports betting is done today by providing more accurate predictions and fairer odds for everyone involved.
Numerous innovative technologies are being used to improve sports betting accuracy in a variety of ways. One of the most popular is the use of predictive analytics, which uses data from past games and events to make predictions about future outcomes. This technology can be used to identify trends and patterns that can help bettors make more informed decisions.
Machine learning algorithms are also being used to analyze large amounts of data quickly and accurately, allowing bettors to make better decisions faster. Additionally, artificial intelligence (AI) is being used to create automated systems that can detect potential opportunities for profitable bets before they occur.
Finally, virtual reality (VR) is being utilized by some sportsbooks as a way for customers to experience live sporting events in an immersive environment, giving them a better understanding of how the game will play out and helping them make more accurate wagers.
In conclusion, the impact of innovation and AI on the sports betting industry is significant and far-reaching. With the introduction of new technologies, sports betting has become more efficient and easier to manage, while also providing bettors with more opportunities to gain an edge over the competition.
As such, it is clear that innovation and AI will continue to shape the future of this industry for years to come.
cricket betting algorithmHow to predict cricket scores [Excel + Machine Learning]
Can we predict cricket match score in Excel? Using machine learning, ensemble modeling, multiple regression and Excel formulas we can. This tutorial explains how.
Cricket world cup is on. Both my homes (India & New Zealand) have done well so far in the tournament and if things go OK in the last couple of matches, they should qualify for semi-finals. The games are happening in UK, which is 12 hours behind New Zealand. You know that means?
Yes, lots of sleepless nights in Wellington.
As I watch these games, I notice that every once in a while they show a “score predictor“. It will tell you what the final score could be based on the proceedings of the game so far.
So I thought, hmm, May be I should make one of those in Excel?
That is what I did. I created a machine learning model in Excel to predict cricket score. Sounds interesting? Read on.
If you are thinking “the only cricket that keeps me up all night is the damned chirping one in my basement”, then don’t worry. You need very little knowledge of cricket to understand the techniques. Once you know the ideas you can apply them to many other real life problems like predicting sales next year or student absences next term or electricity usage in the new plant.
Just a quick note if you are not familiar with cricket. In a typical one-day match, two sides compete. The game starts with one of teams batting first and scoring some runs in 50 overs. The next team then tries to beat that target set by first team in 50 overs.
Let’s start by defining the problem. We want to create a cricket score predictor that takes the inputs:
Country playing – CRuns scored so far in the match – RsWickets remaining in the match – WR Overs remaining – OR
Our predictor should tell us what could be the final score at end of 50 overs.
For example, we could ask, “Australia scored 52 runs in 10 overs losing 1 wicket. What would be the final score?” and our predictor can provide a guess – say 305 runs.
The final score of a team in a cricket match depends on many things, including:
The playing team and their batsmenThe opposition team and their bowlers, fieldersThe ground and pitch they play onThe weather and historical weatherCrowd attending the game and how much they are cheeringWhat the umpires had for lunchetc.
If we try to incorporate every little thing that matters, we will never be able to construct our prediction model. So let’s ignore everything except those 4 parameters (C, Rs, Wr, Or) listed above.
Let’s say Rp denotes our predicted Runs. We can define Rp as
Rp = p(C, Rs, Wr, Or)
where
C = CountryRs = Runs already scoredWr = Wickets RemainingOr = Overs Remainingp is a prediction function that does some magic to calculate Rp
But we know that Rp = Rs + something
This is because total runs at the end of 50 overs will be something more than Runs Scored at the time of prediction.
If we can find something our problem is solved. But how?
Run rate is ratio between runs scored and overs completed. So if India scores 342 runs in 50 overs, their run rate is 6.84. As our prediction model is for 50 overs, if we know the Run Rate, we will know final score.
Let’s define few more variables.
Os (Overs so far) = 50 – OrRRs (Run Rate so far) = Rs / (50-Or)RRr (Run Rate for remaining overs) – this should be predicted
Given these variables, we can rewrite Rp (Runs predicted) as
Rp (Runs Predicted) = Rs + Or * RRr
So if we can build a model to predict RRr, we can calculate Predicted score.
We can argue that Run Rate in remaining overs will be a function of (country, run rate so far, overs remaining, wickets remaining)
RRr = f(C, RRs, Wr, Or)
We can further argue that each country has specific strengths and abilities when it comes to batting. So, if we define a set of functions, f1(), f2()…, fn() where fn is
RRr for country n = fn(RRs, Wr, Or)
We can then call the relevant function based on which country we are predicting the score for.
Regression of course. There are many sophisticated machine learning algorithms. But for something straight forward like Run Rate (remaining overs), we can create a simple multiple regression model.
Say RRr can be written as
RRr = m1*RRs + m2*Wr + m3*Or + const
Given a set of training data with RRs, Wr, Or and RRr, we can use LINEST() function in Excel to calculate {m1, m2, m3, const} that fits the sample data. Once we have the multipliers and constant value for each country, we can predict the score for any situation. Its that simple.
As with everything else in life, cricket matches too have significant variability. For that reason, rather than one regression model per country, why not create 10 of them per country and the average the prediction?
As management consultants say,
If this sounds like a bunch of bs, don’t worry. This is an actual machine learning technique known as Ensemble Modeling.
The idea of ensemble modeling is simple. We build multiple models from the training data and then combine the results of all models when making predictions.
See this picture to understand how a typical Ensemble Model works.
This way, we can create more variation in input scenarios and create a robust model.
For the sake of simplicity, let’s say we want to build 10 regression models for each country.
How to aggregate the ensemble model results?
We will end up with 10 predicted Run Rates (Remaining overs). We can simply average these 10 to come up with final prediction. You can also assign weights to the models and do a weighted average. Let’s stick with simple average.
Let’s get building then.
Learn first, Predict next.
That’s it. But if you want more text, here we go. Almost all machine learning models follow this pattern. They look at some data to construct the model. Once that model is ready, we then test it on a different data set to see how satisfactorily it performs. If the results are not up to scratch, we back to step one and fine tune the model.
The easiest of all these is a regression model. That is the same one we will be using too.
There are many fine websites for finding current score or recent match results. But in order to train our model, we need a collection of historical match data by each over. This is notoriously hard to get. Thankfully, there is cricksheet. They have historical one day match data at ball by ball level for 1,400 + matches in CSV format. (here is the downloads page)
Note about data: I noticed that cricsheet doesn’t have all matches data. For example I could not find any 2018 games in the data set I downloaded few days ago. It doesn’t really matter as we are using a large sample of data spanning several years.
As you can see, this data is at a too detailed level than what we need.
So I used Power Query to combine 1,400 files, reshape the data to over by over scores and then calculate total score, overs and wickets at every 5 over interval until end of the game. I then took only the recent 300 games as very old performances have little impact on current scoring patterns.
Sorry for not explaining the Power Query or Excel formula steps. That would get too technical and this post will never end.
Once reshaped, my data looks like this:
We can then derive additional columns,
Last over of the game: =MAXIFS([Over], [Source.Name],[@[Source.Name]], [Country], [@Country])RRs – Run Rate so far: =[@[Cum Runs]]/[@Over]Or – Overs Remaining: =[@[Max Over?]]-[@Over]Wr – Wickets Remaining: =10-[@[Cum Wickets]]Score at end: =SUMIFS formulaRRr – Run Rate (remaining overs)
=([@[Score at end]]-[@[Cum Runs]]) / [@[Overs Remaining]]
Using 2,3,4 & 6 we can create our regression models.
But before we go there, let’s split the data in to training & test data sets. For this example, I choose the latest 50 games as test data set and everything older as training data. Instead of creating two separate tables, I just added a column at the end to look at [Match ID] to tell me whether something is test or training.
Also, we do not need to use last over data for training. At the end of game there is nothing left to predict, so there is no point of using last over data when training the model.
I have added a data point ID as column to this table so I can uniquely identify all data points when sampling training data. It is [Data point number]
Don’t freak out. We are still on topic. Bagging is the technical term for the concept of randomly sampling data, building models and then aggregating (ie bagging) at the end.
Bootstrapping refers to random sampling of data.
Our bootstrap approach is rather simple and naive.
For each country, we want 10 bags – 10 data setsFor each data set, we want a random sample of 50 data pointsWe then create a multiple regression model to fit
RRr = m1*RRs + m2*Wr + m3*Or + const We average all 10 model results when predicting outcomes.
A note on const: When I was building my cricket score prediction models, I realized that setting const=0 gave me a better R2 (ie the model fits well with training data). So I set the 3rd parameter of LINEST() to FALSE (ie no need for const). You may want to keep it on for other types of models.
In my training data from cricsheet, we have 16 countries. That means we need 16*10*50 = 8,000 data points to construct the models.
Using a bunch of RANDBETWEEN, INDEX+MATCH and COUNTIFS, I was able to construct this grid.
Once data grid is ready, we can create a bunch of LINEST() formulas to tell us the multipliers (m1, m2, m3) for each model. This can be done 160 times (each of the 16 countries need 10 models). But I am very lazy. So I used INDEX() formula to fetch arrays of 50 cells so that LINEST results can be tabulated nicely. This is how our multiple regression model looks:
sample-results-ensemble-model-for-cricket-score-prediction
As you can see, our model has very high R2 values. This is promising.
While high R2 values are good, you should not trust the model blindly. You should also check if the relationship between output (Run Rate in remaining overs) and inputs (RRs, Or, Wr) is chance. This can be done by looking at F statistic and F distribution probability. I have not bothered with this step for all of the data, but I did check for few samples to see if the F probability is low (low means relationship is not random).
Learn more about F statistic and how to interpret the results.
As you can see, each model predicts Run Rate (in remaining overs). But we need to predict the score. Given the inputs:
C = CountryRs = Runs already scoredWr = Wickets lost so farOr = Overs Remaining
We can calculate predicted Runs (Rp) as
RRr = average of all 10 country specific predictions (RRs, Wr, Or)Rp = Rs + RRr * Or
In simple words, our final prediction is Runs already scored + average of 10 predicted run rates times remaining overs.
Now that we have our shiny ensemble models, let’s go test them. I have extracted score data from last 50 games by innings. I then filtered away any games with less than 50 overs played (canceled due to rain, chased before the last ball etc.)
This is what we have.
test-data-for-cricket-score-prediction-testing
For prediction, we also need to know what were the runs scored and how many wickets they had in hand at certain over. I started by creating a scrollbar to select the over (any multiple of 5 between 5 and 40). Then we fetch the relevant inputs from test data and run the model against them to calculate predicted score. I then compared this against actual score to see what kind of error and accuracy our model is getting.
test-results-how-to-read-them-and-notes
This involved using some crazy, but fun MMULT and INDEX functions (ofcourse, TRANSPOSE too). It is 2:19 AM as I am typing this. That means, Unfortunately, it is too late in the game to explain the formula logic here, so I will leave it to your imagination.
Here is how our model compares with actual results at 15 overs.
actual-vs-predicted-score-15-overs
And this is how it works after 25, 35 and 40 overs. As you can see, accuracy improves the later in game you ask for prediction.
Right now, as I am typing this, India is playing against Bangladesh. India have score 314 in 50 overs. I wanted to see how our model predicts the score at various points in game. As you can see, it gets a little optimistic (as India didn’t loose a wicket until 30th over) but the prediction gets closer since 35th over.
If you want to examine the calculations, predict your own scores or just want to see how its all done, here is the file.
In the download:
You will find two models, not one. This is because I built two regression models to see which will give better prediction. The one presented in this article gives better results.You will find a simple score predictor too. Enter inputs (Country, runs so far, wickets so far and overs) and it will tell you what the predicted score is.All calculations and data.
Feel free to mash up the data to create your own prediction tool.
I made a short (oh well, 37 minutes long) video explaining the process, machine learning concepts and Excel implementation. Watch it below or see it on Chandoo.org youtube channel.
This is an interesting topic and I am sure you want to know more. See below references to understand the concepts better.
Ensemble modeling:Ensemble modeling & learningEnsemble modeling basics, terminology and sample Python codeUnderstanding bootstarpping techniqueLINEST() & Multiple Regression:LINEST Function – Microsoft HelpLINEST example and explanation from MITLINEST and other forecasting functions in ExcelF statistic:Understanding F statisticExcel features:Power Query for combining filesINDEX functionAlso see:Time series forecasting in Excel
I had so much fun creating this. I did have a few false starts and made models with wrong equations, but eventually came up with something that provides sensible prediction. I am happy with the way it turned out. Although I couldn’t explain every little thing about the model in this post, I hope you are able to fill those gaps in.
Do you like this prediction model in Excel? Are you surprised to see a complex machine learning algorithm implemented in good ol’ spreadsheet? Share your thoughts in the comments.
cricket betting algorithmHow to win and make money betting on Cricket
From a five-day Test match to 20-overs under the floodlights, cricket is a highly nuanced game with many subtelties to each different code. Paul Krishnamurty runs you through the best ways to turn a profit from each different format of the game…
Formats and tournaments
If pressed to name the sport that offers the best trading opportunities, it would be hard to make a stronger case than for cricket. There is a televised match virtually every day, sometimes two or three, and liquidity across a wide variety of markets. Furthermore, this sport involves so many nuances, all of which have profound implications for the outcome of each match and innings. By developing a detailed understanding of those nuances, the well-researched, disciplined trader will soon enjoy a significant advantage compared to most punters.
There are five main formats:
1) Five-day Test Matches involving international sides. These are usually played over a series lasting up to five matches.
2) Four-day matches between English counties. In these cases, broadly the same betting principles apply as for Tests.
3) Fifty-over per side matches, which usually involve international sides. Again these are usually part of a series, or a multi-team tournament such as the World Cup or ICC Trophy.
4) Forty-over per side matches between English counties in the CB40 Trophy. The strategic criteria for trading these is basically the same as for 50 over matches.
5) Twenty-over per side matches. This is a global phenomenon, with four main club tournaments and an international scene. The biggest club tournament is the Indian Premier League, played over six weeks in April. In England there is the Twenty20 Cup. Australia hosts the Big Bash and finally the Caribbean T20. Finally for clubs, there is the Champions League Twenty20, which this year will be in South Africa during October. International sides will compete for the World Cup this September in Sri Lanka.
Most popular markets
Match: Match winner, Test Match End, Completed Match
Innings: Total runs, Session Runs, Top Team Batsman
General Rules
Know the players, their skills-set, preferred formats and conditions
While many players perform across all formats – e.g. Stuart Broad, Chris Gayle, Jacques Kallis – most tend to reserve their best cricket for ideal conditions and format. It is perfectly possible to be the world’s best in one format while a non-entity in others.
For example, West Indian all-rounder Kieran Pollard is one of the most valuable T20 players, a regular star at international tournaments and pretty good over 50 overs too. Yet in Tests, Pollard is an abject failure. Alternatively, Australian captain Michael Clarke is one of the best Test and 50 over batsmen in the world, yet ineffectual at T20.
Only the very best can perform well in all conditions. For example, most batsmen from the Indian sub-continent have failed to make the same impact when touring England, Australia and South Africa. The reason is that they learn the game and play mostly on Indian pitches, which tend to be slow, with much lower bounce than encountered in those other countries. Equally for the same reason, English players have rarely produced their very best on sub-continent pitches.
Learn to read how a pitch will play and whether it will deteriorate.
As illustrated above, pitch conditions are pivotal and reading them is one of the keys to successful betting. Arguably, the pace of the pitch, how much help it offers to bowlers and how quickly it deteriorates will have as great an impact on the outcome of a match or an innings total as the players themselves.
Ideally, one should always have an opinion on whether the pitch will favour batsmen or bowlers and which type; how it will be playing later in the day or in Test matches, tomorrow; what the run-rate will be in both the short-term and over the course of the innings and match.
This subject is a constant matter of discussion among commentators, whose expertise in this regard is invaluable while you learn the art, and a useful extra opinion once it has been mastered.
Understand the impact of the weather
Likewise the overhead conditions not only determine whether there is to be play or not, but the nature of the match. If there’s plenty of cloud cover, swing bowlers will enjoy a marked advantage and batting totals are likely to be substantially lower than average. If the sun is out, those faster bowlers will receive much less assistance, transferring the advantage to the batsman. However constant sunshine will bake and break up the pitch, favouring spinners later in the game.
An extreme example is Headingley, a ground famous for favouring swing bowling, but that has also seen many scores over 500 in good weather. During the 2009 Ashes series, Australia bowled England out in bowler-friendly overcast conditions on the first day for just 102, before making 445 on the same pitch once the sun came out. This despite England being clearly the better side over the course of the series.
When bad weather causes the number of overs to be reduced in one-day matches, a complex formula known as the Duckworth Lewis method is used to reset batting totals. This can have a huge impact on the match winner market and requires monitoring.
Expect dramatic turnarounds and don’t be deterred from taking huge prices
In-running drama is almost a given in this sport, with upsets at huge odds being a regular occurence. In Tests, the draw often trades extremely short before the pitch suddenly deteriorates and teams often fail to chase down supposedly easy targets in all formats.
Equally, the runs total markets regularly see massive upsets. A fairly frequent scenario involves a team losing its last five wickets for less than 50 runs. Alternatively, teams quite regularly hit 70+ runs off the last five overs in T20. In both scenarios, some very short-priced bets will have lost.
Study stats and past results at each ground
Before trading any match, check out past results at the ground in question, using a tool like Stats Guru on www.cricinfo.com. Past trends at virtually every ground will offer useful clues, with regards the match winner, pitch characteristics, innings totals and run-rate. See below for examples.
Tips and Trading Strategies for Test Matches
The formbook stands up, with home advantage particularly significant
Whereas limited overs matches tend to be closely matched, there are many one-sided Test matches. Often analysis of the teams, weather and ground stats will point in one clear direction. When it does, don’t be afraid to take short odds as there are many rock-solid favourites in Test cricket
For the reasons mentioned above, home conditions offer a significant advantage. Consider the results of India, the 50-over world champions and usually a strong challenger for the top Test ranking. Since the beginning of 2009, they’ve won 10 out of 15 home Tests, losing only once. Away from home, they’ve only won four from 20 and lost their last eight to England and Australia. Yet in those two away series, because India are roughly of the same standard in general terms, neither home side was prohibitively priced before landing comprehensive victories.
Even when the odd upset does occur, the clues are often there.?Take South Africa, a top team with a daunting long-term home record at most, if not all, venues. At a time when they were constantly challenging for world number one status, the Proteas lost all four Tests played in Durban between 2009 and 2011. Despite the bad omens, the outsiders started the last two matches as big outsiders, on the basis of wider form at different grounds.
Lay the draw on pitches likely to deteriorate
While being careful not to get caught out by the weather, trading the draw price in-running offers a chance to utilise all that stats and pitch analysis.
Most pitches deteriorate sharply as the pitch gets older, making life tough for batsmen as bounce becomes uneven and cracks aid the spinners. It is perfectly normal for a pitch to yield 400+ runs in the first innings but less than 150 in the fourth. Over the first three days when the pitch is favouring batsmen, the draw price invariably collapses, based on a false assumption that conditions won’t change. When they do, wickets tend to tumble in quick batches, dramatically transforming the match and markets. Matches often produce results despite the draw trading heavily odds-on.
This isn’t, however, a plan to follow blindly. Always maintain a good idea of how many overs remain in the match and whether there will be time for the changes to take effect. Study past innings totals at the ground to identify the scoring trends.
Back ‘Under’ in runs market once partnerships hit milestones
Just as the draw price can over-react to good batting, so too can innings runs markets. If two batsmen establish a decent partnership, their team’s run line will rise with more or less each run. Partnerships can’t last forever though and were to some extent already priced in at the initial quote. When a wicket falls, more often follow as new batsmen struggle to settle. The potential for a batting collapse and dramatic betting turnaround is always live and many odds-on bets are turned over this way. As a rule, it might pay to blindly back unders every time a partnership hits a milestone like 100 or 150.
Tips and Trading Strategies for 40 and 50 Over Matches
Wait until the first innings is complete before taking a strong view on the match winner
Although 50 over matches are famous for nailbiting close finishes and volatile betting, there are many one-sided matches. Sometimes a particularly advantageous toss stacks the odds in one direction. Sometimes the team batting first is bowled out and fails to set a meaningful target.
Until a substantial part of the first innings has been completed and the pitch been assessed, there is great uncertainty about what represents a good total. It makes good sense, therefore, to spend the first part of the match focused on the innings runs markets, fine-tuning that pitch analysis and forming strong opinions for the second innings run chase.
Use the ground stats to study past totals and run-rates
Again, ground trends are pivotal. Study past totals and run-rates at different stages of the innings. Some pitches yield a fast early run-rate, only to slow up later in the innings.
During the last World Cup, run lines repeatedly rose in the first 15 overs as batsmen flourished, only for teams to fall short as the pitch became less conducive to scoring.
Some grounds have small boundaries, offering the potential for very fast scoring during power plays and the end of the innings, but are hard to accumulate runs on during the middle of innings. A recent example came at St Kitts, during the West Indies v New Zealand series. Scoring was moderate in the three first innings played there, averaging around five runs per over. Yet despite limited tail-enders batting during the last five overs, the average rose to nine per over as the short boundaries were regularly cleared.
Tips and Trading Strategies for T20 Matches
Back extremes, especially on run lines
Most of the above analysis applies in equal measure for this shorter format. Again, meticulous study of past ground stats is essential.
One distinction with the 50 over format is teams’ batting strategy. Over the longer format, teams tend to share similar tactics – attack during power plays and at the death, while accumulating singles during the middle overs. In an ideal T20 plan, the team hits regular boundaries from start to finish.
If it comes off, extremely high scores are possible, which will always be available at big prices. If it doesn’t, wickets can tumble fast and a team fall well short of setting a challenging total. Consequently, backing very high or very low totals at big prices often pays.
Pre-innings run lines in IPL tend to average around 160. Of 73 completed first innings during the IPL, seven saw the team fail to score 120, while 19 scored over 180. The big innings tended to come on predictably conducive grounds – in this case Bangalore, Delhi and Jaipur. Any study of past results there would have indicated these were ‘overs’ rather than ‘unders’ grounds.
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