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Sports betting spreadsheet excel

Federica betting 16.07.2021

sports betting spreadsheet excel

This is a user-friendly spreadsheet that works in both Excel and Google Sheets to track your profits. It's extremely user friendly and is mostly automated to. You can log bets placed at any bookmaker and keep a very detailed record of them. We would have a sports betting market and each member you bet tracker. The worksheet tracks your betting history and provides performance data with numerous filters as well as a summary graph. There are seven worksheets as. PADDY POWER BB BETTING

The program counts the W's and L's and calculates their percentages. BAT as well. The program generates the combinations and saves them to a disk file. Combination generator for sports betting. The player selects any number of games, then the number of outcomes. Normally, there are either 2 or 3 outcomes to a game: 1 for home team victory; 2 for visitor's victory; 0 for a tie.

This is the case of soccer games. In other cases, such as the American football, there are only 2 outcomes. The favorite team is required to win by more than the point spread. The outcome can be symbolized by 1. The program also calculates the winning chance for each case. TEAMS is not compressed zipped and you can run it directly.

You just need to enter the teams playing, past results, and other key data that correlates to future performance. Are you a baseball betting fan? If so, then this will be the perfect template for you. Our mlb baseball sports betting excel template will help you keep track of all your bets and results. Just enter the data for each game, and watch as the spreadsheet calculates your total earnings at the end of each day.

When using a excel based model, you create an efficient handicapping system. Sign up for a trial today or keep reading how to bet the spread in baseball.

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Once you have downloaded the spreadsheet, you will find the preset examples that are put there to guide you. After you have learned the ropes of the program, make sure all pre-existing entries are removed so the results may be accurate and respond to your data. Also, keep in mind not to delete a column or a tab, regardless of whether you are using them or not, since any change might affect the formulas and results.

All your betting results originate from the Settings tab, so it is crucial you enter all the necessary data to get the accurate calculations of your winnings. No matter if you bet on NASCAR, NHL or a fantasy game, the sports bet tracker lets you customize it to include: Different sports you bet on All of your bookmakers Various types of bets Wagers Under the Bets section, you will see what type of bets you are most successful in, for example, single or handicap bets.

The Use of Withdrawals and Deposits Tab With this Excel betting spreadsheet, it has never been easier to track how much you win and how much you lose. The Deposits and Withdrawals tab makes sure miscalculations and overspending are avoided while showing how big your winnings are. It is worth noting that the dollar sign will be added automatically after you have typed in the amount.

The Availability of Funds Tab Available Funds tab shows how much you have in each account and how much credit you have with each of the bookmakers, including bonuses and free bets. This option gives way for a fast in-play bet without having to waste any time on making deposits or doing anything else. If you start using more and more bonuses and bookmakers, you will notice that using at least an excel spreadsheet is a must.

How to use a matched betting spreadsheet? The easiest way of tracking matched betting is using a prebuilt excel template that includes every information about: Bookmakers used Date of registering the account Username and password The amounts of deposit, actual balance, moned to withdraw Bonus types and the amount of them Sports used Pending bets Profits realized with that bookmaker Using a worksheet that includes your betting records will optimize your workflow.

In the long run, you can experience that the amount of time searching for data will get very short. A lot of the paid and also free oddsmatcher software are offering built-in spreadsheets that can automatically track your winnings. You can place bets through their services and the spreadsheet or database will record them. No matter what strategy or workflow you are following, you will need a database of your bets and bonuses.

To be honest, using a service like this could help you a lot, but I like to store my data and history in the same worksheet. How to make a simple betting spreadsheet? To build a database and a good betting template, the first thing you need to do is to sort out every data type you will need.

In the list above I already mentioned almost all of them you could need for matched betting. For other kinds of strategies markets, odds and leagues could be necessary too. I have built an excel file that will help you record your bets and all information regarding your matched betting activity.

I think using a database that can be found only on your computer is way more secure than uploading it to another site. A lot of bettors prefer to track their bets at specialized services. Those can offer more tools without investing too much time in logging your bets. I think that experienced bettors or people who know how to use excel should build their own spreadsheets. Below you can see a screenshot of the spreadsheet that is downloadable for free.

I think the data given is enough for every matched bettor. The information you need to introduce will help you track your income, bonuses, and bookies connected to your matched betting activity. As additional information, I would like to mention that you can secure every profit tracker that includes personal data.

You can protect the excel file with a password and nobody can access the data to your accounts. If you check other tracking services or excel files created for tracking bets, you will notice that most of them can seem very complicated, hard to understand and to use. Here you can download this spreadsheet for free, by clicking on this button. For a lot of bettors using these matched betting templates can offer motivation. Some of them will include most of your bets, your stakes, and other types of data.

From my experience, these kinds of databases are useful only for beginner bettors. If you are increasing the number of bookmakers you will use simultaneously, the simpler the database, the faster the work will be. I also have a dedicated article for casino offer matched betting , that can be practised with the help of Profit Accumulator.

Team Profit matched betting spreadsheet Another great spreadsheet that can serve you well is the one created by TeamProfit. The more complicated a spreadsheet is, the harder it is to use. TeamProfit has created the spreadsheet template to: Help you track your profits and losses Include your balance at every bookmaker Help calculate your stakes Link to a ton of bookmakers that offer bonuses You can download it here. Sam Sportssmartbetting I have been a professional sports bettor for almost 8 years.

This blog was created to share my experience and knowledge. I started building up my betting capital with matched betting, followed by arbitrage betting and switched to full-time value betting. Each software and betting strategy I promote was tested or used for a longer period by myself.

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Oh, this player is hot right now, these guys can't win a game, et cetera. And didn't really have too much interest in other sports at the time. And it started to sink in that there's more to this than just picking who you think is likely to win and there's got to be more to this than just your own gut shot opinion.

Pythagoras theorem is something that I used a lot of in electrical trade school because it's used to calculate variable power reactive power in power triangles. And so, it clicked for me, I realized, oh- there's a mathematical basis to this. I'm going to probably need to use math to solve this problem just like you do with electrical problems and that really is what led me down the rabbit hole of statistical analysis and eventually model making.

Ben: So, you've got … There seems there's a lot of loose ends that were going on here. You said, that's statistical background, the work that you did, obviously a bit of relative success to do with poker and interest in sports and everything like that and was it that initial change that you said, right, I'm going to switch up the approach and I'm going to start to take this seriously. Was it easy to stick to that or did you find yourself falling back into those gut picks or trying to trust your knowledge over what a model said or what the data said?

Andrew Mack: That's a great question. I would say that I made most mistakes that I think a lot of beginners make. So definitely, it was not a linear path to success. I certainly didn't have this realization and then never strayed from it. Definitely, I had a few setbacks. Ultimately when you're a beginner, you just, you have, I think what's been turned unconscious incompetence, right.

You don't know what you don't know. And so, you think, okay, well these gut shot picks aren't doing very well, but this, what about this trend stuff? What about this team's record against the spread, for example, or something or their last five games or the last 10 games? Read: Failure: The secret to success?

I definitely looked at every different Avenue and as a result fell into some of the traps that I think a lot of people just starting to fall into in terms of not being able to properly identify, okay, this is probably not helpful and here's why.

Tried a bunch of different things and many of them didn't work. And unfortunately, with sports betting you largely have to build your own toolbox or arsenal yourself and part of that is taking a few hits along the way to learn some things the hard way, and certainly that was the case for me.

Ben: Yeah, it's probably quite easy for you to look back now and think, God, I was square back then and I didn't know what I was doing. But at that point in time you said, you were losing a bit of money and stuff like that.

It's going to turn around. What was your mindset? Did you think you were going to win money and you were doing it the right way? Andrew Mack: From that time, I recall being really frustrated when I didn't win. I actually had an expectation of winning, but I didn't have a reasonable basis for that and I don't know that I was even aware of the sharp square, categorization or dichotomy at that point. I was really just thinking, why isn't this working? There's got to be a way to do this.

I just haven't found it yet. And I tried to keep a positive attitude but ultimately, like I said, I just didn't know what I didn't know. I wasn't … There were so many elements to sports betting, whether it's staking size, [or] whether it's the proper statistical techniques that can help you actually make a reasonably accurate forecast. But it was definitely a rocky road in the beginning there for sure. Ben: And say obviously for me, I'm putting these things together and you've got the previous work and the sports fan getting serious about banging and then this law school thing then comes along as well.

Is that just something that at the time were you thinking career wise, betting you were getting serious but not really seeing it as an avenue to earn a living and the law school kind of thing was just an interest in terms of career development? Andrew Mack: So, law school was an interesting story for me. I really enjoyed being an electrician. You really have to get a sense of satisfaction from building something and doing it well and using math to solve problems. I really enjoyed the work.

What was becoming an issue for me was that my body was breaking down. So, the injuries were piling up and I'm not that old, at the time I think I was 32 and there's the stories that I could tell about some of those things would curl your hair, straighten it back out again.

I'd finished, my last day of the week would be Friday. On Saturday morning, I used to take about a thousand milligrams of ibuprofen so that I could close my hands enough to hold a cup of coffee. You wake up on the weekends and your knees hurt and your elbows hurt and your hands are swollen. And like I said, some of the injuries were piling up.

I broke my foot. I broke my big toe, I tore my right rotator cuff. So, some of those things, being a bit of a bigger guy, six foot three pounds, you're asked on a job site to do a lot of the really, really heavy lifting. If there's a piece of equipment that nobody else can move, that's something that you're going to be tasked with doing. And I was happy to do that, but those kinds of things started to take a toll and I started thinking to myself, if this is what this is like at 32 with the injuries and the relative chronic pain that I was having.

What would it look like at 42 or 52 or 62, and I realized that the odds were pretty good that, I wasn't going to be able to do a whole lot physically 10, 20 years down the road. So, if I was thinking about different options, maybe I should look at that now. That's when I really looked into law school. I had a very, very strong LSAT and as a result was admitted and my plan really was … Two things were at the forefront of my mind. The first one was worst case scenario, I come out the other side of this with a law degree.

I can practice law and that's something that I can do without racking up all of these injuries for the next 20, 30 years. But the other idea that was in the back of my mind was law school will also give you some time to think. It will give you some time to refine the models that you've been working on, and if that goes well there is a good possibility that you can take a serious shot at betting [on] sports professionally. So, those were the two driving factors for me.

Ben: So, you put your body under enough strengths, then you decided to put your mind under some strain with the perils of law, schooling and betting? Andrew Mack: In a market proven to be highly efficient how realistic is this? Winners Welcome Sign up here Log in here Ben: I think we've got some really good insight into kind of where you've come from in your career and in general as well in terms of betting. So, let's talk about more your day to day life now. So, you obviously keep yourself very busy with everything that's going on at the moment.

Has that always been the case? Is that part of your makeup as a person? Andrew Mack: I don't think, actually. I think that this is just, I'm in a unique position here where law school is going was well and I found a way to structure my classes spread out throughout the course of the day in such a way that I'm able to squeeze a little bit more out of the time that I have, and where I'm at with my modelling I'm also able to squeeze in the masters as well.

It's pretty tight, there are some days where I might skip a class in order to finish an assignment for the masters or vice versa. But I'm able to make it all work. I would say that I'm definitely running pretty close to maxed out and this is definitely not … I don't think that it's ideal. I think that sports betting generally would be better for me if I had a little bit less on my plate and for that reason, I'm looking forward to finish with law school so that I can really just focus on it a lot more.

Ben: When it comes to the balance of law school and betting and the statistical side of things, do they complement each other in terms of a skill set or is it almost a hindrance that you've got to shift from this mindset of law brain to then this data-driven betting brain? How does that work?

Andrew Mack: Yeah, that's a really good question. I would say that, there's two elements to that. Two sides to that coin. The first part is that there are elements about law school that do complement. And I would say that with respect to the law, the understanding the learning of law, it's an inherently probabilistic discipline. Even though it's not science-based, you might consider it the art of probabilistic thinking rather than the science, more mathematics of it.

It's always a question of stronger or weaker arguments or more likely or less likely to succeed in any given legal context. And so, I think that type of thinking generally is helpful for sports betting because it helps to prevent against overconfidence. Where you realize that even when you have an exceedingly strong case or a very strong argument, there are counterpoints to consider and you really shouldn't think of anything as a quote or quote lock.

And that's as true in laws as it is in sports betting even when most of the law is on your side or in the case of betting, most of the probability is on your side. The part that's difficult, at least for me is that legal argumentation is in many ways the reverse process of the scientific process because you actually start with the outcome that you want and then you work with the case law and the arguments and the fact pattern that's available to lead to your foregone conclusion.

And so, you worked from the end goal back through the facts, which is the opposite of what we're trying to do with most statistical analysis. We don't want to have a preconceived notion of what's going to happen. We need to test things and we need to question assumptions and things of that nature.

And so, I think that sometimes for me that's a lot to keep in your mind simultaneously. The two very, very different modes of thinking. But that being said, I think that learning to think critically and analytically, whether you're using it for law or sports betting is a very useful skill. Ben: And then when do you have time to put into the betting side of things? What's the process that you're going through? Is it, how much time are we waiting to the building of models or the development of models versus the actual act of running those models, finding the bets and placing the bets?

Andrew Mack: I would say that it's very heavy on the creating and testing and finding and once you get them up and running there's not a whole lot to it other than, you then become much more involved in monitoring, changing game conditions like a lineup changes or injuries and market price movements and a little bit of calculation with regard to staking size, so if you want to use fractional Kelly on multiple simultaneous bets, there's some additional calculations to be done there.

But other than that, I would say that running the models is relatively light on the workload compared to the building of the model. And I think that's pretty straightforward to most listeners because most model ideas just don't work out. So, you need to have a lot of them and you need to keep trying things and that's time-intensive.

I wouldn't say that it's completely a brute force type of thing, but in many ways, it can feel like that when you've been working on a model for 40 hours and it turns out that it's not very good, it can feel a little bit of a brute force endeavour. Ben: And then you said that there's so much that goes into whether it's lineup changes or the odds simply moving in the market moving and stuff like that. If you're also limited for time are you manually betting or is it an automated process for you through APIs and things like that?

I know Pinnacle has a great, a great API package for doing things like that, but I haven't done that just yet because I want to personally inspect and approve every bet that my model would suggest betting on before it actually goes through. I don't … That's just me though.

I don't know that's necessarily better to do it that way. I definitely can tell you that, once I have more free time and law school is finished, I do plan on transitioning to more of a full automation just to lighten some of the workload that I have for myself.

Some things I have are automated now, which is nice, but definitely I need to move more in that direction to become more efficient with my time. Read: Using push and pull spots in betting And to speak back to your point about time limitations the net effect of that at this point is that basically there are some days where I'm just not able to bet because I'm not able to actually take a look at all the necessary factors and place the bet at the appropriate time.

And so, there's a couple of days a week where I might just not be able to bet that day. Because of my other time commitments. So, I try to get it in a few days of the week and the weekends at the moment. Ben: Right. We want to get on to modelling in your book very shortly. As I said, a couple more questions just for me to get to know you and your betting as it is at the moment. Are you, obviously it's modelled, but are you betting on specific markets or specific sports at the moment?

Andrew Mack: Yes. As of right now, I'm betting predominantly on the NHL as it just opened up recently. And I think a little bit of CFL and I think that's most of it right now as of this period in the year. With regards to other seasons in the year, other things that I've done or looked at.. MLB was a tough season for me, mostly because I've had some trouble forecasting the bullpen and so, while I continue to try and work out the kinks on the full game models, most of my betting for the MLB that's been successful l has been prop bet.

So, strikes hits, runs errors as a prop home runs, runs in the first inning and a little bit of a first five innings because usually the starting pitchers that are still playing at that point, and so I've had a little bit more success with that. But really over the course of the year, anything that I think I might have an edge on, I will take a look at, I do some NFL props as well, pass attempts, passing yards, receiving yards, rushing yards, touchdowns, a little bit of small league Euro basketball as has been mentioned in the book.

I occasionally find a little bit of value there as well and I think that's most of what I'm up to these days. Ben: So the leagues themselves what many people know to be very efficient leagues are obviously major betting leagues for a lot of bookmakers. And it seems that you then digging around into the markets within those that perhaps might be a little less efficient, but have you scaled up with your modelling and maybe I don't know, gone from KHL or Euro league basketball and moved up through those levels to where you're now [at] a point, you need those high limits to get your action down or is it just you're dedicating time to those markets and that's why you're betting on them?

Andrew Mack: A little bit of both. I would think that, well, I suppose I should say that as your models get more sophisticated and more nuanced, you can progressively work your way up in more and more efficient markets. It's certainly not the place that you should start, which is a point that I continue to repeat over and over again on Twitter and in the book. But you can definitely get there provided that your models have the required level of sophistication. And you made a really good point with regards to these sharp markets, not every sub-market or derivative market inside.

What we would consider largely to be a shift market is at the same level of efficiency. And so, as a result of that, there are frequently opportunities that present themselves. Props are a really great example of that. Read: How to bet on basketball And with regards to the other things, like, definitely started out with smaller basketball leagues like the Icelandic women's basketball league and some other, European basketball leagues.

And then worked my way up with the hockey model. I never actually focused much on the KHL. I really just started with the NHL and eventually progressed it to the point where it's showing good value in producing good results even though the market is quite sharp. So, slightly different approaches for each market. Although I would say that basketball and baseball are very sharp and require quite a serious commitment to your model's sophistication. Ben: Well, I've heard a lot of talk about modelling.

Let's get into it and discuss your book as well and a little bit more detailed. So, you published Statistical Sports Models in Excel earlier this year and for anyone that hasn't read the book, could you just maybe give us a brief intro into it and tell us what it's about and why people should read it?

Statistical Sports Models in Excel is essentially a book that I wish existed when I first got into modelling. It's basically designed to be a crash course in foundational statistical modelling techniques for the explicit purpose of sports betting with the heavy technical language and statistical formulas removed to make it easier for beginners to understand.

And I think it will give the readers some new model ideas, techniques to make their own forecasts for games and ways to help determine if a model that they've made is working out or not. Ben: Yeah, I can certainly testify the book sets out to do exactly what it says on the tin.

I'm far from an expert on Excel and it certainly taught me a lot in a fairly short space of time. And with Excel being in the title - is that when we talk about your models and what you're doing with modelling in general. Is that what you're using or are you now looking at different programming languages?

Andrew Mack: Well as I mentioned in the book, one of the reasons that I felt safe, I guess you will, to write the book is because I have moved on to R and to a lesser extent Python and my SQL databases and things like that. So, I'm using a more sophisticated process now. And obviously as your model becomes more sophisticated or requires more sophistication tools like that are only going to help you.

So, some of the things that I was doing in Excel previously I'm not really using anymore. And so, felt free to be able to share those with other people and have them help other people learn and get up to speed more quickly, but at the same time not really have to reveal too, too much though what my current betting models are looking like or doing and so that was the impetus for the book. Ben: I think if you, you mentioned Excel or Python and stuff like that, you quickly get these battle lines that are drawn in the sand and people are quick to defend whatever they use the most.

And I guess there's someone that's used those three that we just talked about. Could you maybe touch upon the strengths and weaknesses for each, for anyone that maybe doesn't know or only has knowledge of one thing? Yeah, I would say in some ways certainly with R and Python, it's very much a Coke versus Pepsi argument.

Everyone has maybe a personal favourite, but whether or not that's totally justified, other than purely familiarity as not totally certain. I would say that what's great about Excel is that it's hyper visual. Its point and click interface makes it very easy for someone that is just getting started to understand what's going on behind the scenes. And the most important thing that you could do is pop the hood and understand what's going on with the engine you want to be able to click on a cell and be able to see the formulas that that cell is using because it helps you understand the processes and the functions, and all of that is going to build your understanding of what's happening.

And I think that that is one of the understated strengths of Excel is that it's so visual that it's easy to spot. It's easy to troubleshoot mistakes that you've made. It's easy to see what's going on and all of that can provide you with a greater understanding. I would say, that its weakness is probably data wrangling because as many people know, when you get any amount, any type of sports betting data, you are going to have to do a tremendous amount of data wrangling to turn that into usable information.

So, it's going to have a blank inputs. So, certain players didn't play that game or they didn't record a certain statistic that game, they didn't get any rebounds or they didn't get any assists or whatever the data is there's going to be empty cells, empty values, there's going to be outliers. Read: The problem with Outliers So maybe inputs that aren't totally helpful to what it is that you're trying to do.

So, generally with data science, you want to deal with your empty values or no values. You want to deal with your outliers. You ultimately would like it to be formatted in a way where statistical analysis is easy to do. And so how you structure the columns and the rows is very important. All of that in Excel, it can be quite challenging and so when you get into the really heavy work with thousands and thousands of data points, that part can be a little bit cumbersome in Excel certainly.

Those two things become fairly tedious in Excel and that's usually when most people start to think about maybe other options for doing it. So that's Excel, I would say, I've heard mixed things about R in terms of some people really think that it's quite user friendly, some people think that it is almost uninterpretable.

And a lot of that seems to have to do with how much of a computer science background do you have. The computer science background people that I've talked to seem to love Python and have a certain amount of disdain for R just because of the way that the inputs are set up. I personally found R to be very user friendly. Although I don't have computer science background, so that might be an element to it. I found that the coding was fairly approachable and made a certain amount of sense.

I would say that its strength is that it was built primarily for academics. And academics usually pioneer the leading-edge packages. So, whatever … if there's a new machine learning algorithm that's just come out, it's very likely to show up on R first, so, you have a lot of really totally free cutting-edge tools that you can use with R.

And I think that that makes it very, very useful for sports betting. Scraping, web scraping if you want to scrape odds and things like that. I don't know if it's quite as good as Python. I think that Python is a little bit easier to use for scraping web data and that to me is the real split. I think that R is really good for the machine learning and the statistical analysis.

It's a little bit more cumbersome for the scraping. Python is also very good for the machine learning and statistical analysis as well and a little bit better for web scraping. So really, either or whatever works for people. Ben: I think anyone, no matter what their preference, they can't have any complaints with that. I guess one of the questions I often have with these languages is that people tend to say that they're similar to a spoken language in the sense that once you learn one, it then becomes a lot easier to learn another.

Is that something that you'd agree with? Andrew Mack: I don't know if I would totally agree with that. But I found R was a lot easier to learn than Python and that's just me. Other people would obviously have different experiences. I found that Python was a little more, it seemed a little more technical to me, I guess would be the word I would use to describe it. Certainly not that you can't learn it and certainly not that there isn't some level of crossover because there is, but R and Python do have different ways of doing things, assigning variable names and other things like that and you get used to one and then you try to switch to another, sometimes, you make a few mistakes here and there and you go back and you fix it and carry on.

So, I don't know if … I guess in one sentence, in terms of getting your mind used to thinking in code, it is helpful. You learn one and then it becomes easier to learn another one. But obviously the details will be different for each of those respective programming languages. Ben: So, if someone … let's imagine someone's hovering over the purchase button on their book, obviously they need to be aware that as good as your book is, it's not a case of buying it, reading it and you're set to go, you're going to start making money from sports betting and through the modelling side of things.

What are the Andrew Mack: Skills and traits. I would say first I would say positivity, which may surprise some people, but I think that, if you don't approach it with the right attitude, you're going to give up before you ever even get a sniff of success. So, I think that's actually a very undervalued attribute.

A curiosity I would say, because really when you're trying to develop things for yourself, you have to ask instead of having a negative attitude, you predetermining why certain things won't work or are unlikely to work. It's much better and more helpful to be curious and to ask yourself, well, what if this worked or what if we could do things like this and you try it. So, a curiosity and a willingness to explore and experiment even at the risk of a foolish or silly experiment, I think is a really, really positive trait.

Read Bankroll management: Odds, edge and variance Also, the ability to think critically and analytically and to consider contrary or contemporaneous evidence to the contrary of why something might or might not be causally related or connected. Those are all good things to have as well and really just a desire to keep learning and keep improving because the moment that you think that you've finished learning is a dangerous moment for a modeller, because it's very much an arms race when you're building a model that is trying to outpace the market.

They haven't quite clicked them into place in the past. The thing is people want money to almost find that motivation. But if someone isn't at that point yet, how do you, what would you say to someone that says, look, I'm really struggling.

I know what I need to do, but I just can't get that final little push to do it. Is there anything you'd say to them that would help them get there? Andrew Mack: Yeah. There's a quote from the founder of IBM that my dad used to say when we were little kids. That was, if you want to double your rate of success, you need to double your rate of failure. Which was, I always thought it was an interesting quote. There's something about that where you really have to get your hands dirty and you have to start making some mistakes because when you make mistakes and you learn from them, you will improve.

Whatever it is that you're doing. You continue to keep reading, you continue to keep trying to find new ideas, but above all, keep trying things and keep making mistakes because every mistake that you make, you're going to learn from that. You're going to realize, okay, that didn't work and you're going to build for yourself almost like a database in your mind of experimental ideas that worked or didn't work or looked promising.

And when you start amassing those, you start having better ideas. And so, a lot of people seem to think that, maybe they can just think about one thing coated, put it all together and boom, it's going to work and instant glory and riches and that's just really not the way that it works. You really have to, you have to learn from your mistakes and you have to make a lot of mistakes in order to get to where you want to be. I think there's another expression that an expert is someone that's made every mistake that can be made in a very narrow field.

And I think that there's a lot of truth to that. Ben: Props to your dad, Andrew. I think he grew up in the right household. So, outside of inspirational IBM quotes. And if we're looking at tools or resources that people might use to not shift that mindset but to help them develop the actual skills to build models and help them to find success in betting. Are there any websites or blogs or any material out there that you've thought that was really useful in your journey?

Andrew Mack: I would say there is one, and forgive me for starting off maybe at the more complicated end, but there is an eBook currently out by a guy named Jason Brownley from Australia and it's called Machine Learning Mastery. And it's an eBook series on both R and Python. And the premise of his eBook is essentially that developers don't always understand the statistical nuances of machine learning.

So he put together these eBooks where he gives you a crash course in machine learning. And then walks you through templates of code to run all of the various machine-learning models. So they're there, it's almost like a basic template for every machine learning model that you might want to start with, whether it's regression or classification.

And he goes through a number of different things and basically the example code that he gives you … he uses some very simple example data sets, but the example code is worth a hundred times the price of the eBook because you have an example to visually see how to work this in. And you can take out the example data, plug in sports data and just try it and you will immediately begin slowly understanding how you might be able to apply machine learning to a sports data set.

And I think that's probably one of the best machine learning resources that's available. Very helpful and definitely helped me to get up to speed in both R and Python for the actual machine learning element of it. So, I would recommend that if people are interested in that they should definitely check it out. With regards to some of the more basic stuff it … Google is your friend. I think that that's been said before.

I think Rob Pizzola said that in his chat with you. But it's so true. Google is your friend, if you have a little notepad and you write down statistical terms and things that seem interesting. Read: Does intuition have a place in sports betting?

And then just start Googling them and trying to look for research papers or videos or anything that you can find that might help explain that. That's a very useful, you might want to know. What is a Bradley Terry model? What is a Glicko rating system? How is it different from an ELO rating system? What is the TrueSkill rating? What is multi culinary? What does a P value mean? How is it different from a T stat value? There are so many questions that you can ask and really how helpful Google is to you is very dependent on how good the questions that you ask are.

So that was definitely a huge point for me and I think that's something that a lot of people should focus on as well if they're trying to improve. What else did I do? I pretty much bought every statistical modelling and sports betting book that I can get my hands on.

This way you can capitalize on what you're good at, and modify your strategy in areas you struggle. The only way you can do this is by tracking your sports bets in a bet tracking spreadsheet or bet tracking app. Your sportsbook provides very little information beyond a list of bets you've won and lost so it's important to separately track your bets to help you learn more. Also, enter your unit size. Step 4 - Fill out the bet information for each bet Enter in the amount you wagered, the odds, the result, the category, and which sportsbook you bet on.

Step 5 - The spreadsheet does the rest for you! The spreadsheet automatically fills in all the other relevant columns for you and gives you a summary of your strengths and weaknesses. This template makes it easy for you to simply enter in the date, teams playing, wager amount odds, and bet type, and the spreadsheet will fill out the rest.

This means staying consistent with your wager size to keep yourself from large losses. It also means staying consistent with leagues you follow more closely.

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How To Track All Your Bets, Win/Loss %, And ROI With A Bet Tracker Spreadsheet (FREE DOWNLOAD!)

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