A Different Kind of System…

So I’ve got this great new system I wanted to share: it wins 57% of the time with a great sample size of 915 trades, only risks a meager $100 a trade, has a max drawdown of $700, has made money 9 out of the past 10 years, has a nice steady equity curve, and has made $10,207 after slippage and commissions since January 2005. Interested? Of course you are, but first let’s conduct a thought experiment before I share the details of the system with you.

What exactly is a “system”? What exactly is an “edge”? Better yet, what exactly is our goal in “system trading”? Can we create systems for other markets, outside of the stock market? Well, I’m glad you asked…

My own personal definition of a system is simply, a defined set of rules that allows us to recognize when we have a perceived edge in a market place, any market place. An edge is a mathematical calculation that shows us quantitatively that, over time, we should expect to win more than we lose. So to get to the real questions, what is the goal of this pursuit? Well I don’t know about you, but I’d like to make a little money, while having fun of course, developing the systems. So if we’re in this to make money, then doesn’t it beg the question, what other “markets” can be exploited? What other systems can we create and prove we have an edge? Take a walk with me, to a completely different, yet oh so familiar market.

The system I described in the opening doesn’t trade in the world’s financial markets, but it does “trade” in an equally efficient, and equally exploitable market….the sports betting market. Now, before you jump to the conclusion that sports betting is just gambling, allow me to open your eyes. Yes, going to a casino and playing craps or 3-card stud or roulette is gambling, the house has a known edge that it knows will win over the long haul. But what if I told you that you could be the house in the world of sports betting. With the use and help of big data, we can test every theory and scenario under the sun, and find those not so elusive edges.

Human psychology not only plays a huge part in the world’s financial markets, but to an even larger degree in the sports betting markets. If you’ve read this site for any period of time, it’s been well documented that the S&P has substantial mean reverting tendencies. It is almost always better to buy on weakness and sell on strength no matter what timeframe you’re trading on, or what system you’re using. But why is that? Do you think human psychology has anything to do with it? Of course it does, it’s the fear at the major market bottoms, and the greed/euphoria at the market tops that have supplied ample opportunity (aka edges) for those that have understood how the masses think. It’s no different in the sports betting market place, the masses like to bet favorites, home teams, and the overs, this is well documented. The masses, or the “pubic” as we like to call them in the sports betting world, loves a winner and hates a loser. The public has a very short term memory, and a team that was great yesterday is terrible today after losing last night’s game. (How many times can you recall the latest hot stock go from the penthouse to the outhouse literally overnight, and then back to the penthouse after everyone is short, it happens all the time!) It’s also well documented that the “public” consistently loses over the long run, this makes sense otherwise the bookmakers wouldn’t exist, so doing the opposite of what the public likes to do is where we need to be looking for our edges. It’s a common misconception that bookmakers want 50-50 action on every game, then don’t. For example, the New England Patriots are a “sexy” and “winning” team, every week the bookmakers know that the “public” will bet on the Patriots every week no matter what, so to counteract this, they will slightly increase the “fair market value” line away from the Patriots. If 70% of the bets are on the Patriots every week, the sports book obviously needs to win more than 50% of the time. So taking the other side of “sexy” teams is generally the better bet. Anyways, it’s simple ideas like these that are the basis to finding your edge, and building your system.

So let’s jump right in and build the system in the opening from the ground up (this system is from sportsinsights opensource “ThinkTank” by user zaf). Data mined approaches are popular in the financial markets, and rightfully so in many cases as there is just so much more data that can be used. For sports betting models, I think it’s important that your edge be empirical and makes sense in the real world; you should be able to explain to someone why your edge exists. The system from the opening is for the NBA (National Basketball Association), so we will only be testing this theory on NBA games from January 2005-Present (that’s as far back as my data goes). And to keep it simple, we will assume a flat $100 wager per game. Again using the theory of our human psychology that: A. the public has a very short term memory and B. Likes to bet home teams, let’s see if we can find an edge.

So everyone knows what a huge advantage home court advantage is in the NBA right? Well, not so much anymore: (chart from Tom Haberstroh)

NBA-Home-Court-Win 1

I spy a trend, there are a multitude of reasons for this trend that are beyond our scope, but the 40 year trend is very real, and the data proves this. Let’s add to the fact that the public likes to bet home teams anyway, and I think you know the first place we should be looking for our system. Visiting teams. Here is what your account would look like if you bet $100 dollars on every single home team every night, or every single visiting team every night since January 2005, the results are shocking:

Results-02

If you’re a home team kind of guy, you’re probably not a happy camper, as you’ve lost more than $60,000 just betting $100 a game!! Now on the flip side, if all you ever did was bet on the visiting team, you would actually be up $1,800, an almost unbelievable contrast. So now that we know where to start, here is what betting on every single visiting team since 2005 looks like in an equity curve:

EQ-Curve-03

So now that we know solely betting the visiting team is an edge all by itself, let’s try and improve upon that edge. We’ll start by testing NBA games where the team that we will be betting on today lost its previous game.

EQ-Curve-04

As you can see, just this filter alone gives us a 2,843-2711 record, but because of the juice, or vig, which is the house’s take (generally speaking, the house will take about 5-10 percent of your winnings each bet, so if you bet $100 and win, you will only be paid $95-$90 in winnings…think of it as the slippage and commissions of sports betting) this filter alone isn’t enough as we lose $353 dollars overall. Next, let’s try adding another filter. Let’s say that the team we are betting on is a good team (we’ll define “good” as winning 55% or more of your games), but just so happened to lose their previous game (penthouse to the outhouse). We can add a filter of “team winning percentage” to the previous filter of “losing their previous game”:

EQ-Curve-05

So that certainly helps, we now have a record of 1099-1001, good for 52.3%, an improved equity curve, and after the juice is taken out, we’ve made $4,847. But we’ve still got about a 3- year period where we are under water, so I think we can do better. So now armed with the above system, let’s take a peek to see if that visitor/home edge still exists:

Results-06The above image says it all, betting on “good” home teams coming off of a loss would lose you your lunch money, whereas betting on “good” visiting teams coming off a loss would make you a nice profit even after the juice is taken out. Below is the equity curve:

EQ-Curve-07

So there she is, three very simple filters that make real world sense, and make real world money. Using a system such as this, you in essence become the house, your expectancy says that you will win over the long haul. And the beautiful part about all of this is that there are literally thousands of systems just like this that are not hard to find if you’re willing to look. Once you have a certain number of systems, the name of the game then becomes the number of plays you can make every night. If you have systems for all 6 major US sports (NBA, NCAA BBall, NFL, NCAA Fball, NHL, MLB) and you have an average edge of 5-7%, over the course of say 2500 bets a year, that adds up really quick. Cocktail napkin math here: 2500 bets $100 a bet= $250,000, $250,000.05= $12,500 a year, but you can of course bet much, much larger than $100 a bet, and if you have a really good year, you can win almost 60% of your bets (don’t expect that though). Normally when I find a new system, I let it “soak” or sit for one entire season as a kind of walk forward/out of sample test. But because I found this system a year and half ago, I consider the out of sample test complete.

In conclusion, I hope I at least opened your eyes to a different kind of market, and a different kind of system. There are so many parallels to sports betting (not gambling) and systems trading I don’t know how anyone can be interested in one without being interested in the other. If you start to do your own testing in the sports betting market, there are a couple things you should always keep in mind:

  1. Make sure you can explain at a dinner party why your system works.
  2. The less filters the better, any system with over 5 filters I personally have a hard time trusting.
  3. The greater the sample size the better.
  4. More bets is almost always a good thing, it means your system is robust, and likely not curve fitted.
  5. Keep in mind that depending on the sport, the number of acceptable bets in a system will vary greatly. 60 for the NFL might be enough, but 250 for MLB is likely not enough.
  6. If a filter doesn’t REALLY help, leave it out!
  7. Let a system “soak” for a season before putting it to work, and bet small the first time you go live.
  8. Try to make robust filters, if changing a filter by a tiny fraction greatly impacts the results, then the filter likely has little or no predictive value.

-Ben Little

About the Author Ben Little

Ben has been a private trader for over 7 years and is now a TradeStation Trading App Developer. Finding and exploiting edges is his game, as he’s not only a trader but a professional sports handicapper. You can find him on Twitter @TRUmav for financial commentary, or @BtheHouse for sports betting commentary. If your interested in more models like the one above, check out BtheHouse.com. Ben always enjoys sharing and brainstorming ideas with like-minded traders, bettors, and investors.

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  • Dar says:

    Hi Ben,

    Thanks very much for sharing your insight. I’m sure this article has piqued a lot of interest. Coming from a financial trading background I would have a few questions on this sports system…

    [1] How is slippage calculated on the available odds, or put another way, how do you know that the historical odds represent the odds you would get in live trading? For instance, if these odds are taken at a particular time prior to each game, then to replicate the results you would need to place bets at that time as well. I’m guessing that closer to tip-off, gamblers may get more bullish about the home team which would mean waiting to place bets as late as possible is optimal in this case.

    [2] Do the commission costs include the cost of Sportsbook Insider? Based on the example bet size, this would have cost more than the net PnL over the period shown.

    Many thanks,
    Dar.

  • Dar says:

    Finally…

    [3] How does this system work at the start of the season when there are no win/loss percentages available yet to decide which teams are “good” visitors? Would you use the previous season’s statistics or would you wait until a statistically significant (>30?) number of games had been played in the current season?

    Many thanks,
    Dar.

  • Ben Little says:

    Hi Dar, thanks for the comments and all great questions. I’ll answer them in the order you asked:
    1. The software I use for the testing records the exact odds at the time of the close of the line. So if at tipoff the line was -110, then the software records that in the data. So if a winning $100 bet was placed, it will record a net profit of $91 (It’s actually $90.91, but it rounds up .09 cents). Your correct that in order to replicate the results, its important to wait as close to gametime as possible to place the bet. All the data in this system uses Pinnacle closing lines as the actual number bet. If you bet sports for a living, its important to have several accounts at several different books, so that you can actually outperform the backtested results by consistently getting the best line available.
    2. When I say “slippage and commission” in reference to betting models, its just an analogy. There are always overhead cost you have to overcome, so no the commission costs does not include overhead like subscription costs and the like. And it’s no different in financial models, we dont include the cost of TradeStations platform in any calculations of systems. Remember, this is but one system…to give you an example, I personally run 22 separate systems JUST for NCAA basketball.
    3. There are several ways to approach defining what a “good” visitor is, and it really comes down to personal preference. For simplicity sake in this article I started the backtest on game 4 of the regular season, but the performance is actually improved if you wait until game 14 to start to using this particular model. You can certainly use previous seasons winning percentage in the beginning of the current season and I suspect that it would work just fine, though I have not personally tested it.

    • Dar says:

      Thanks for taking the time to answer my questions Ben. It must be a busy month for your 22 NCAA systems! Go Badgers!

  • Hi Ben,

    In the beginning I was skeptical but after reading your article I think you have an edge and I also like your idea of “simple filters that make real world sense”. This is what I use in my analysis, system development and trading. Given that the edge is small, do you think that revealing it may actually eliminate it? Have you done any statistical analysis to determine whether the edge is significant?

    I cannot calculate the Sharpe ratio of the fourth equity curve but if you have it then multiply it by the square root of 10 and it will give you an estimate of the t-statistic. That will tell you how many standard deviations the observed profitability is from the null hypothesis of zero profitability. It would be great to know if you have that number.

    Good article.

  • Ben says:

    Hi Michael, great questions and thanks for the kind words. As far as the systems edge fading, in full disclosure, I didnt exactly give away the family jewels, as I would consider this a pretty average model based on pure performance. But like I discussed in the article, human psychology of the masses has always provided an edge in any market. Everyone knows that when the S&P gets a 50% haircut, you should be buying, but the masses still don’t do it, and generally they are more tempted to sell then to buy (and vice versa during the big bull markets). Its no secret that the books will shade points to the un-sexy teams, but the masses still lay the points on sexy, big favorites even if they have a negative expectancy. And to circle back to the article again, I think its only human to overweight the most recent results of, well, anything…sporting events included, which is what this model tries to capitalize on. Cognitive biases are the bookermakers, (and hopefully ours as well) best friends, here is great link on the many, many cognitive biases that humans naturally have, and when I re-read the article I can instantly think of examples in my own life where I have confronted many of these (I hate to fly, which is probability neglect): http://io9.com/5974468/the-most-common-cognitive-biases-that-prevent-you-from-being-rational

    As far as the sharpe ratio, I’ve attempted to use financial ratios on the sports betting models before but was generally unimpressed with the information it conveyed to me. I’m a big fan of confidence intervals for the financial models I’ve built, but they dont provide much value when I will generally discard any sports betting system that has more then 2 losing seasons in the past 10 years, makes most of its money off 1 excellent season or has any substantial draw downs in the backtesting. I’m certainly not naive enough to believe that all edges last forever, and I keep close records of max drawdowns on every system and will sideline it if that level is breached. I find myself obsessing over bankroll management much more then anything else. As you said, I think that by creating simple, robust filters that make real world sense, it helps to largely eliminate the issues of curve fitting, historical bias, etc. But that being said (sorry for the long winded reply), I’m happy to provide any numbers from the system if you would like to do further analysis.

    P.S. For anyone else reading this comment, one measure I do really like for sports betting models is “Margin of Victory” or just “Margin”. Basically if the system is on average, covering the spread by only 0.5 points or less (for basketball), that’s generally a red flag. Obviously the higher the margin the better, and a “good” margin will vary from sport to sport.

    • Thanks Ben. If you could provide the mean and standard deviation of returns of the fourth equity curve that would be great.

      • Ben says:

        The Mean is 932, standard deviation is 1160. The yearly returns in order from smallest to largest is:
        -98, -42, 102, 283, 360, 371, 852, 890, 1752, 2085, 3704. This includes the current season (-98) which still has about 6 weeks left in the regular season.

        • Thanks Ben. The sample is small and the true standard deviation may not be know. I suspect it is not known and it is much higher. Regardless, the numbers indicate a borderline significant result but all fitted or simulated data always exhibit high significance and this is a main problem of post hoc analysis.

          • Ben says:

            thanks Michael, I would concur that the true standard deviation probably isnt known at this point.

          • Ben says:

            Hi Michael, I stumbled upon this article that goes into further detail of human psychology in the betting markets (and has several other good links to studies inside the article). But it goes on to discussion exactly why 65 percent of all bets are on the favorites, and why this is. Anyways just wanted to share it as it kind of ties back into the article and your question of the edge fading (and its an interesting look into human nature), as it explains better then I could as to why some edges may never fade which is crazy to think.

            http://fivethirtyeight.com/features/why-people-bet-on-the-favorite-even-when-the-spread-favors-the-underdog/

  • Samuel says:

    Hi Ben,

    Interesting stuff you are writing about. I have a question about this. What kind of software are you using to test and backtest your results?

    /Samuel

  • Ben says:

    Hi Samuel, I use Sportsinsights.com for most of my testing. But the software does have limiting factors so I also do a lot of testing in excel

  • TRADERPROFIT says:

    I live in Las Vegas and have these comments:

    1) You could use a variety of closing lines

    2) You are looking at a period of time where this works, but ignoring what appears to be a period when it didn’t (before 1995)
    3) All of your bets are illegal
    4) You are not accounting for the risk the sportsbook closes and keeps your money
    5) You are assuming games are not fixed

    A few years ago I looked at some interesting stats on the theoretical win rate of sport books in NV v. the actual in rate.
    at 11/10. The actual hold was 2.5 in pro football and as low as .4 in college basketball. I can only see 2 explanations: 1) some people win 2)games are fixed

    With college BB I’d say it’s highly likely a number of games are fixed and also highly likely some gamblers have better information.

    There was a several year period of time where sports books would not take bets on NBA totals except in parlays, other than $300 limit at the then Stardust and $1000 at the Mirage because they lost a number of millions to the “computer group.”

    In any case my argument is for fundamental analysis.

    Have you actually made money for years with this, or just tested it?

  • TRADERPROFIT says:

    Oh, and good luck with this. Really.
    I’ve been trading and sports betting for 35 years.

    By far, trading is much easier because the spreads between buying and selling prices are far less than sports. There are also participants who do not act for psychological reasons, but rather purely mechanical ones such as an institution that isn’t allowed to hold junk debt being forced to sell when debt is downgraded.
    In my book, the other side of that trade is what an “edge” is all about.

  • Ben says:

    Hi Traderprofit, and thanks for the comments. As far as some of your concerns, I’ll address them in the order you listed them.

    1. Yes you are correct that there are many, many sportsbooks that will have differing lines as there closing lines, but this is actually a good thing, not a bad thing. Like I mentioned in a previous comment, I find in my own record keeping that I actually outperform most of the time because I have access to several different accounts there by allowing me to get the best line possible instead of just the line that is recorded for the testing. This in and of itself is an edge. Also the lines are not greatly different wherever you look, at most the biggest differences you see 90% of the time is 1 point, and a lot of this is because books, to a certain extend, want to balance out a little bit if they are really heavy on one side, but again, this is a good thing for us.
    2.My data only goes back to 2005 for basketball, so I cant speak to the performance before that time. If you have data that goes back that far though I would love to see either it, or the testing you did. But I would be cautious drawing conclusions from data that old for most sports, as rules change, and the sports change over time. The NFL is really a completely different league then it was a mere 25 years ago.
    3. Yes, online betting in most US states is illegal, and it is VERY important to know your local laws or if betting is illegal in your state or country. I certainly do not condone breaking the law, and neither does this site. On a separate note, I do believe that online betting will once again be legal in most US states in the coming years, the NBA commissioner has official come out in support of federalized legalization (not state by state legalization), and I think its just a matter of time before the other leagues follow suit.
    4.This is not really a concern, as you could make this argument about any financial institution that holds your money. Just as with any other service, its important to do your homework and make sure that you are dealing with a book that has a good reputation and a long history.
    5. Also not an issue, I know the NBA had the issue with the ref several years ago, but those are very isolated incidents, and if you make 2500 bets a year like I referenced in the article, it doesnt really matter if 1 or 2 of them are fixed. And this is also one of the main reason why the NBA commissioner is in favor of federalized legalization so things like that can be monitored directly by the leagues. I think to assume that anymore then 1% of all US sports games are fixed would be a really, really bold assumption. As far as how much the sportsbooks actually make, I think your very, very low on the estimates. I havent looked in quite some time, but generally for basketball the hold is between 5-10% a season. They all do just fine, I can assure you.
    Its interesting that you think trading is much easier then sports-betting, because I would argue the exact opposite, but I’m sure we could find a whole range of people that would fall all along that spectrum. I think mechanical systems are much easier regardless of if its for trading or sports-betting which is what this great site is all about, and why I wanted to write the article. Thanks again for the comments, and good luck in your trading and betting.

  • TRADERPROFIT says:

    Sorry if I wasn’t clear. Those hold figures are for straight bets. I believe the hold on parlays was about 17% last football season.

    I disagree the risk of a book absconding is negligible, and the illegality is a federal issue , in that you are using the internet in interstate commerce. It’s highly unlikely a bettor is going to be prosecuted, however.

    The late Jackie Gaughan, who owned the El Cortez in Las Vegas, once told me he thought there was at least one college basketball game fixed every night. That’s why he had low limits, and I think it’s a factor in the low hold for that sport, which is even less than baseball–despite narrower baseball odds. I can name on both hands the number of college BB fixes that came to light in the past 20 years. Think about how many were never caught.
    I guess this roughly equates to insider trading, but that isn’t relevant to whether I make money in the market.
    In any case, based on my participation on both sports betting and trading, I have a 400x initial investment bankroll, but in sports I have a loss. A big loss.

    That’s just my experience.

  • Ben says:

    Hi Traderprofit, since I had not looked in quite some time, I wanted to make sure I wasnt way off on the hold percentages.I went and looked what the exact hold percentages were on all sports for all Nevada books statewide the past 12 months, they are as follows according to the Nevada Gaming Revenue report:
    Football: 6.61%
    Basketball: 5.03%
    Baseball: 2.94%
    Parlay Cards: 38.45%
    All sports combined: 5.93%
    The win amount was $234 million for all the non-restricted locations statewide the past 12 months. Not to mention March is historically the best month for all books, and these reports end on 2/1/2014. Here is the reference: http://gaming.nv.gov/modules/showdocument.aspx?documentid=9752

    And you kind of made my point for me on the fixed games aspect, just like insider trading, “isnt relevant to whether I make money in the market.” The model in the article is tested over the exact period when Tim Donaghy was fixing games, it just doesnt matter to the individual bettors.

  • traderprofit says:

    I do see what you are talking about and this was an exceptional year. Note:The “Win Percent” for games provides a ratio which has been adjusted for effects
    of credit play.” I’m not sure what the implications are there. I’ve had a credit sports account in the past.

    I can’t recall offhand from what year I got those stats , but i didn’t pull them out of thin air. I’m sure it was from an article in the Las Vegas Review Journal some time ago.
    So, point conceded as to the past few years. Football win % was up 30% YOY.

    Still, I guess my question is whether you have acted on this info and made money or simply tested it.

    I’ll try to find the data I was providing.

  • Ben says:

    The only point I was making is that basketball makes a heck of a lot more the 0.4%. I just went & looked at the last 10 years of hold % and there are none under 4% for basketball. You can look at every year here going back to 1990: http://gaming.nv.gov/index.aspx?page=149

    I unfortunately live in the United States (not in Nevada) so I will respectfully abstain from your later question. But I have colleagues in London and Nevada that do very, very well betting models exactly like this, and including this particular model.

  • TRADERPROFIT says:

    hmmm. I sit corrected. The figure I saw was college hoops, but they don’t break that out on these reports and the other numbers I saw were much lower also. I guess I liked them so I didn’t check them.
    .
    POINT CONCEEDED.
    I didn’t ask if you acted on the data illegally. For all I know you could fly here every weekend.

    All right, so I ask now how may people do you know making a mid six to seven figure income with what could be called technical or quant analysis? T
    he reason I ask is I know quite a few people who started with 25k …or even a few thousand in the market, and who are multimillionaires. I don’t know any sports players like that, but I am sure there are some.

  • TRADERPROFIT says:

    Looked back to five years after I moved here in 1998, and it’s interesting how hold was below theoretical for a few years, with a loss for the season in baseball.
    There’s even a year there’s a loss in pari-mutuel. That would seem almost impossible.

  • Ben says:

    There is for sure a “size cap” in most sports betting that doesnt exist in financial markets. For most college basketball games, 2-3k is the max most books will let you bet on any one game, but you can again have multiple accounts. But its different everywhere you go, I know a book just took a 50k bet on Kentucky to win it all on there futures book, so it just depends. I know several guys and gals that make 6 figures every year solely on quant sports models, though I dont personally know any that make 7 figures a year, but like you said, I’m sure there are plenty out there. Bill Walters and the computer group that you alluded to earlier would certainly fall into that camp.

  • Ben says:

    Traderprofit, one other thing I forgot to mention for the reason we probably know many more wealthy people in finance then in sports betting, is probably from, simply, leverage. There really isnt any leverage in the sports betting world, yes you can have a credit account, but thats still a cash account in essence. Whereas leverage is literally everywhere in finance, options, futures, even the vast majority of equities accounts use some kind of margin. Its that leverage that allows for such rapid growth of capital, where in sports betting you have to have the cash to put up. Anyways just food for thought, thanks for the comments again!

  • John Lewis says:

    Ben,

    Thanks for this great article, I totally agree with you the least amount of indicators, the better your odd are… and the importance of finding the Edge on what ever trading system you are using.

  • traderprofit says:

    Ben,
    Leverage can kill too.I’ve had some big losses in the past 16 years when M&A deals blew up. I was leveraged, trying to make a 6 cent arb spread on one deal x40000 shares on each side. 9/11 happened, and the deal broke up costing me $6 per share.

    Still, I think markets are narrower, deeper, more tax efficient, and more profitable. Finding an edge is definitely the key, but eventually others will arbitrage your edge away.
    I think my entire edge was a better trading platform providing information not available on a retail trading platform, and access to safe strategies that could not be executed by a retail customer because of margin requirements.
    There’s that, and the crash of 08-09, where there was a lot of forced selling without regard to “reality.” What I mean by that term can best be explained by the 1987 market crash, where put options on the S&P 100 became so expensive the market would have had to fall below 0 for a buyer to make money. Banks stopped financing puts for market makers and the entire system almost imploded.

  • Ben says:

    Leverage is no doubt a double edged sword…but a sword none the less. Its a weapon, that if used properly, can help you make much more money then if you didnt have it. Image if you had to put up all the capital just to trade 1 contract of the big S&P with no leverage, that would be over half a million dollars just to trade 1 contract! There just isnt any equivalent in sports betting.

  • Love this out of the box thinking (to abuse a modern cliche). Equally brilliant were many of the comments…I don’t remember the last time I read the comments section of a blog with equal gusto as the original content. There’s a lot of smart people out there. Putting this blog in my feed immediately.

    • Ben Little says:

      Thanks Matt! I agree all the questions and comments added a lot of value to the original article. In a world where most comment sections are mind numbing at best and crawling with faceless trolls, the readers of STS are always thought provoking and freely share their ideas and expertise.

  • evo34 says:

    Do you have any evidence that you are good at what you do? I.e., where is your third-party monitored track record?

    It’s interesting (to use a polite term) that you are bulled up on a strategy that had a record year last year and this year is losing money. Sports markets change in months, not years, these days and your example has all the markings of formerly effective system that the market has already responded to. In fact, everyone and their brother was already using this system on opening day. Hence, the edge evaporation.

    Finally, on your web site, you claim to “know [your] EXACT edge every single time”. This is a giant red flag, as it indicates you are incredibly overconfident, a slimy marketer, or (probably) both. No one knows his exact edge going forward. You can try to estimate it based on history, but that’s all it is – an estimate.

    Source: 20 years of successful sports handicapping, the last 8 of which have been monitored by respected firms.

  • evo34 says:

    So Ben, are you the user “zaf” from Sportsinsights.com? If not, you blatantly lifted the system, “Good Teams Off Loss,” he/she posted there on 12/6/13 in the Think Thank (SI’s system sharing section for members).

  • Ben says:

    Hi evo34, a couple things….1. The strategy is down 5 units this year so far, the very first year (when very few people probably used it, if any), it was down more then that, and has had several, several drawdowns of equal proportion. So to say that it doesnt work after a -5 unit season would not be very smart (to use a polite term). In fact, I created a variation of this core idea which is up 15 units this year. (and I have shared it with the sportsinsight community for anyone to copy) 2. I can show you plenty of examples where a system has a losing season, only to rebound and have a way better year the following year. In fact, that is the exact human psychology that this system capitalizes on (GOOD team off of a loss). Going past the historical max drawdown is a different issue. 3. Everyone knows your edge is just an estimate of what has happened previously, even with extended walk forward testing, ANY system can fail. 4. If you actually read the article, you’ll see I said “I found this system” not “I created it.” This system is open source and free to copy for anyone with a sportinsight account, just like the one I shared. But I will gladly credit “zaf” if that will make you happy 5. The systems record every single pick every made, which is exactly what a track record is 6. This entire article was done to show how mechanical systems can be created and built for markets outside of the financial markets, and nothing else.

  • evo34 says:

    “Finding” a system and then publishing it for personal gain without crediting the source is dishonest, at best. What is truly dishonest is that the entire article is written as if you are are creating the system before the reader’s eyes piece by piece. The truth is that you took a finished system and are claiming it as your own. How many of the multi-$1,000 systems on your site were lifted from SI, vs. created from the ground up? People usually don’t sit well with others profiting from selling their work.

  • Ben says:

    Well, again, I’m not selling this system, nor claiming it as my own, nor do I sell any that are open source on sportsinsight. I give 100% money back guarantees on everything from picks to systems, and I’m the only person I know of that does that. I put my REAL name on everything I do, and I dont hide behind an alias or user name.

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