Monday, 24 June 2019

17 Leagues, One Season, One Result

As promised, here are the results of backing the Draw in the 2018-19 season. 

As was the case from 2012-2018, overall we again see the expected improvement as the matches become more competitive. 

Coming in to this season, the evidence indicated staying away from the smaller leagues of Belgium, Netherlands, Portugal and Scotland, as well as England's League One.

How did these leagues fare last season? More of the same essentially. 

Prior to this season, the profitable leagues were the top two tiers of the Big Five countries, and again backing the Draw in all competitive matches in these 10 leagues was profitable:

As most readers will know, this season saw a record low strike rate for Draws in the English Premier League, the fewest since 1931-32 which most readers won't remember.

Even with the EPL's 14.43 point loss at the 'Difference less than .25' level, the overall return of 3.2% for the 'Top 10' was the same as for 2012-18.

For serious bettors, the biggest concern should be the increase in over-round last season. It's a topic covered previously in the blog, but every one of the 17 leagues saw an increase this season, with the EPL at an average of 103.5%. That may not seem like a lot, but it makes a huge difference over time and it's a worrying trend.

The full seven season summary looks like this:
In 2017, I referenced David Sumpter's conclusion, based on five EPL seasons 2011-2016, that:
It turns out that when two well-matched teams meet (i.e. the probability of a home win is only slightly bigger than the probability of away win) then draws are under-priced.
The seven seasons for which we have reliable data show that this continues to be true in the big leagues, but not in the fringe leagues.  

Just as I thought I was done, and could enjoy the summer, Joe threw another idea out there:
Looking at the average prices for the Over / Under for the EPL from last season, we're looking at an over-round of 105.2% which might be a problem. That there is a correlation between Unders and Draws will not be a surprise to readers of this blog but it might be an interesting exercise to look at some more data.   

Saturday, 22 June 2019

17 Leagues, 6 Seasons, One Result

A few days ago, Joe tweeted the above message. 

The 17 leagues are comprised of the top two tiers of the Big Five leagues (England, France, Germany, Italy and Spain), plus Leagues One, Two and National in England, and the top leagues in Belgium, Netherlands, Portugal and Scotland. 

Not surprisingly, each league has its idiosyncrasies, not to mention varying over-rounds, and Joe followed up with: 

Before I get to last season, here are the numbers for the seasons for which we have Pinnacle's Closing Prices which are available at Joseph Buchdahl's Football-Data.co.uk site.

Some additional clarifications are that:

  • The seasons covered are the six seasons from 2012-13 to 2017-18
  • All Profit and Loss calculations use Pinnacle's Closing Prices
  • Any matches where Closing prices are missing have been excluded
  • 'Competitive' is defined as matches where no team has a 'true' win probability greater than 0.5
  • 'True' means after the over-round has been removed, i.e. the sum of the probabilities equals one. They are not truly true, but they are close.
First, the overall story, and I hope the picture is self-explanatory.

From the top, we have 40,524 matches and backing the Draw in each would have lost you 2.3%, which is basically the bookmaker's vig. 

There are faster ways of losing money than backing the Draw, but we can do better.

The next block shows the outcome of backing the Draw only in competitive matches, and the loss in this category is 1.4%.

Then I looked at matches where the two teams are within 25% of each other in terms of win probability, and finally at the relatively exclusive category where teams are within 10% of each other.

To keep Joe somewhat happy, 2018-19 is still to come, here are the numbers for Belgium and Netherlands:
As Joe suspected, these leagues do not follow the overall pattern, in fact the Netherlands results are the exact opposite of the overall results. Just back the Draw when a team is odds-on, and count the money, except that the market seems to have corrected since 2015, so this strategy is not recommended.

The only other league which is upside down like this is League One, although here we go from bad to worst with poor results across the board.
The other English leagues all follow the expected pattern, with the Premier League noticeably strong, something that will come as no surprise to readers of this blog.

For the Big Five leagues, the results for the top two levels are:

I'll update these results with 2018-19 in the next few days. 

Meanwhile, if anyone is interested in detailed results from any of the other leagues, let me know.

Tuesday, 18 June 2019

A Tale of Two Podcasts

It was the best of podcasts, it was the worst of podcasts.

Well worth 50 minutes of your time is the Pinnacle Podcast with Rufus Peabody. Rufus focuses on three sports, two of which align with my sports markets of interest, and his comments confirm my belief that profitability is all about exploiting inefficiencies in the betting markets, that the reason these inefficiencies exist is because of biases, and that you need to think creatively and logically as markets sharpen and the number of people who are "good with numbers" continues to climb.

Also interesting to me was that Rufus, like myself, finds the challenge of beating the markets, i.e. the competition, more of an incentive than the money itself. As I wrote more than ten years ago:
I was thinking in the shower this morning, (yes, once a week, whether I need one or not), Betfair really is the ultimate video game. I've never been one for games, (friends at work spend hours playing Call of Duty - why? What's the point?), but in many ways the exchanges are one big on-line game. It's me versus an unknown opponent. My opinion versus yours, except in this game the points are real money.
Or as Warren Buffett describes beating the markets:
"In a sense, the game that I'm in gets more interesting all the time. It's a competitive game, it's a big game, and I enjoy the game a lot"
It's not often that I can compare myself with Warren Buffett.

There's plenty of other content that is worth listening to, if only to understand the level of competition you are up against if you are trying to create a model, and how the markets have changed over the years, although Rufus is a relative youngster!  

He also claims at the end to not be a speaker, and hopes people will make it through the podcast, but for me it was easy. 

This was a very good listen.

On the other hand, not worth 50 seconds of your time, are the same old nonsensical ramblings from Mel "Scientia Trader" who is back spouting his rubbish after the "huge" set back of losing £472.35 in March.

Mel is most definitely not a speaker, but appears to be blissfully unaware of this, offering viewers / listeners a 100 minute mono-tonal lecture, littered throughout with "you knows", or at least the parts I selected at random were, and remarkably absolutely no content of interest. I take my hat off to anyone who can sit through this, and if there is anything of interest, please let me know along with the time. 

One unintentionally amusing moment I did stumble upon is when Mel explains how he had a problem registering with bet brokers who don't accept UK customers, but then suddenly realised he had dual citizenship. Problem solved! 

Amazing stuff. I mean, who among us hasn't suddenly remembered that we have dual citizenship when it comes to betting? While it is true that this is a useful advantage, it's not likely true that its value only dawns on a bettor several years into his betting adventure. As a plot twist, this one is a little bit of a stretch.

I shall leave to the reader to draw their own conclusions as to why the loss of such a small amount, a miniscule draw-down from the profits already (supposedly) reaped, would trigger a ten week disappearance, and the need to "Reflect, Recover and Restart", but to be clear, the idea that anyone can consistently have a win rate of 55% to 60% for an outcome with a probability of .357 (i.e. 2.8 in decimal odds) is complete and utter nonsense.
Some markets might be inefficient, but none are quite that inefficient, or would remain so for long, and as I have written before:
This risk to reward ratio implies an average price of 2.8, i.e. a win probability of 35.7% for his bets, so anyone achieving a 50-60% win rate at that price would clearly, and rapidly, be on the way to a fortune and keeping very quiet about it.
If, by some miracle, Mel really does reside in a world where the laws of probability actually suspend themselves [say Hi to Tony while you're there], the idea that the amount risked should be 1.5% is similarly, literally incredible. 

Using Kelly, the suggested stake with this kind of an edge is over 28%, which should be a clue that something doesn't quite add up! 

Even a more modest quarter-Kelly suggests a stake in excess of 7%, so either Mel is terribly confused about the edge he has or he's clueless about how to make the most from it. 

The evidence suggests that both are true.

I did attempt to listen to a few minutes to see if Mel had learned anything, but once the name of fantasist Adam Heathcote was mentioned as a source of inspiration, it confirmed that thinking logically is still not Mel's strong suit. 

Not a good listen - unless you have trouble sleeping.

"You can't speak butterfly language to a caterpillar." - Unknown   

Sunday, 16 June 2019

Flipping Variables

I mentioned recently the importance of having a logical reason why a system should work, rather than using 'data mining' to find a back-fitted system which has no predictive value at all.

For example, yesterday's post shared an idea for a system which takes advantage of the idea that the issue of time zones in the NBA may not be fully understood by the public, and as hopefully everyone understands by now, these situations offer an opportunity, at least in the short-term.

Contrast the solid logic and rationale of this idea with my tongue in cheek example of:

Back the Home Favourite in an American League game when the pitcher lost last time out, the visiting team won their last game but gave up two runs or more in the third, the temperature was 73F or hotter, and the game is being played on a Thursday afternoon.
One of the forums I frequent regularly has such 'systems' recommended by contributors, but sadly most have them have multiple conditions applied. For most of these, there is simply no logic or rationale as to why this condition might lead to a market inefficiency. Fortunately, there is the occasional nugget that does makes sense, but these tend to be rare.

There was a voice of reason who jumped in on one 'idea' as it spiraled out of control, and explained the problem quite neatly, and while I have taken the liberty of correcting errors of spelling, punctuation and grammar to improve readability, the gist of the comment remains valid.

I need to speak out in terms of experience here before someone gets hurt.
The system included here has little likelihood of future success, and here is why. My experience, which is plenty, has shown me that when you back-fit a situation with so many conditions (at least 12 here), you're no longer predictive in terms of the future, but creating the most perfect flow chart of the past.
Most of these systems are built on finding something with a modest ROI, and then experimenting with variables, meaningful, or meaningless, adding only those that make the situation appear better.
If you experiment with a lot of variables, then you will "stumble" into those that make a certain situation look better, but in the process, you "contaminate" future predictability.
What you are left with is thinking and believing you have found the holy grail of sports betting, only to be fooled by a false premise of profitability. I guarantee you over the next 100 games, the win rate will be closer to a 0% ROI than to the back-fitted ROI.
We are putting faith in a system that is built upon 12-15 conditions, many of which are used, not out of known predictive advantages, but out of anything that improves the win rate. By definition you will find something that is better than the one condition variable.
Think of this. You flipped a coin 200 times, and without adding any filters your results were 100 heads and 100 tails, no predictability of future flips.
You video taped every flip. Now you go back and analyse what happened. You notice that when you flipped with your left hand, heads came up 55 times out of 100, while when you flipped with your right hand, heads came up just 45.
So you add the variable, if flipped with left hand, 55% of the time you get heads!
Further looks see that if you placed the coin in your left hand from your right hand you got 30 heads and 20 tails, but when you picked the coin up instead without any use of your right hand, you got 25 heads and 25 tails. So you're now up to a way to get 60% heads!
Next, you noticed if you paused for more than 10 seconds after placing the coin in your left hand from your right, you got 17 heads and just 8 tails. You now have a situation that generates 68% heads, just by flipping the coin from your left hand after placing it there from your right hand, and waiting at least 10 seconds before you flipped it.
So my question is:
If you then flipped the coin 300 more times, doing everything the way you did to get 68%, what percentage of heads is expected from the 300 flips?
The answer is 50%!
The variables used above were selected not based on anything that is predictive, but based on anything that made the system look better!
THAT IS THE PROBLEM!
The heads scenario here was built the same way, none of the added conditions are predictive in any way!
The moral of the story is this:
Build a concept on known +EV variables, not a system built to make +EV concepts.
What do I mean by meaningful variables? When you do a search in a given sport with one variable, and it shows an advantage, then you have found a meaningful variable.
Build a pile of meaningful variables for a given sport, then stack the meaningful variables, to get meaningful situations.
Hope that helps everyone here.
I should add that it is quite possible to identify a variable that appears meaningless but for which it later turns out there was a valid reason all along. A Tweet from A Lucky A Day referenced the 15th a few weeks ago.
Some readers may be familiar with the apparent illogical pattern in the 15th round of Sumo Wrestling competitions mentioned in Freakonomics where, in certain contests, the supposedly 'weaker' wrestler was winning 80% of the contents.
In a sumo tournament, all wrestlers in the top division compete in 15 matches and face demotion if they do not win at least eight of them.
The sumo community is very close-knit, and the wrestlers at the top levels tend to know each other well. The authors looked at the final match, and considered the case of a wrestler with seven wins, seven losses, and one fight to go, fighting against an 8–6 wrestler.
Statistically, the 7–7 wrestler should have a slightly below even chance, since the 8–6 wrestler is slightly better. However, the 7–7 wrestler actually wins around 80% of the time. Levitt uses this statistic and other data gleaned from sumo wrestling matches, along with the effect that allegations of corruption have on match results, to conclude that those who already have 8 wins collude with those who are 7–7 and let them win, since they have already secured their position for the following tournament.
There was an underlying reason for this apparently illogical finding, even if the reason wasn't understood until later, so it's important not to dismiss every impacting variable as meaningless. 

Saturday, 15 June 2019

Fast and Three

So in the space of two days, both the NHL and NBA seasons are over, and it's all about baseball for the summer. 

At the start of the 2017-18 NBA season, I shared some thoughts regarding the totals markets that even the most critical of readers would struggle to find fault with.  

Actually, the only fault was that I underestimated how rapidly the totals would increase, and the suggested entry point of 215.5 soon became unmanageable, although had you been on all of them, you'd have been rewarded, but 431 bets over six months is a lot of work.

For the 2018-19 season, the initial band I targeted turned out to be even more ridiculously low, resulting in a whopping 701 bets! 
Whatever your entry point, the key to this idea is that the public have been slow to adjust to changes in the game, and the resulting higher totals.

In the five seasons between 2010-11 and 2014-15, the total was never set at 230 or higher.

In 2015-16, there were five such occurrences, in 2016-17 there were 30, and last season there were 233. That's fairly conclusive evidence, although sceptics might want to wait a few more years to confirm. 

Even the traditionally lower scoring Eastern Conference has joined the party. Having never previously had a total set higher than 230 points, 2018-19 saw no fewer than 42 such totals set, and as you might have guessed, the public balked at such a novelty, biases kicked in, and consequently Overs went 26-16.

The number of three-point baskets is the biggest factor in this increase in points. Looking at the regular season, in 2005-06, the average number of 3-pointers scored was 5.7 from 16 attempts; numbers which exactly doubled in 2018-19 to 11.4 and 32. These numbers have climbed steadily over the past seven seasons, and show no sign of slowing down.

One thing that is perhaps a little surprising is that the strike rate from 3-point attempts is pretty much the same as it was 25 years ago. Last season's 35.5% is actually slightly lower than the 35.9% of 1994-95.

The 3-point line was introduced in 1979-90, and it took teams a while to get up to that percentage, but since then it has remained in the 33.9% to 36.7% range.

The reason there are more attempts these past few seasons, is that the pace of the game (as measured by possessions per 48 minutes) is increasing. Last season, for the first time in 30 years, the number of possessions hit 100. This was to be expected, as the pre-season rule change reducing the shot clock from 24 seconds to 14 seconds after an offensive rebound was implemented for this purpose. 

While offensive rebounds stayed about the same, defensive rebounds hit an all-time high, the result of more missed shots.   

While there is a limit as to how high the pace can go, we do know that it has been as high as 107.8 and as that was in 1973-74, the first season the statistic was recorded, it's quite likely to have been higher at some point prior to that. 

For the second consecutive season, and the only two seasons since the introduction of the 3 point line, the ratio of free throws to field goals was less than 1:5. 

Bottom line is that I don't see the totals as peaking just yet. There's room for the pace to increase, and for improvements in shooting accuracy. Kyle Korver's 53.6% in 2009-10 is the benchmark.

The only question is when will the markets catch up.  

Following Eastern teams when playing in the West as underdogs had a winning record, but a small loss, while the 'tired' dogs eked out a small profit. 
The idea of 'Eastern' teams being value when playing 'Western' teams received a somewhat wider audience than this blog when the Guardian published an article on the topic at the start of last season (no wonder edges disappear!) including this comment that readers of this blog were already aware of:
It’s just one more home court advantage for West Coast teams, hosting sleepy teams from the East.
One problem with defining teams as Eastern or Western is that a match between teams from the two conferences isn't necessarily a match between teams from different time zones, with the Central Time Zone having both 'Eastern' and 'Western' teams. The Eastern Conference spans two time zones, while the Western Conference spans three.

So an Eastern (Central Time Zone) team playing at a Western (Central Time Zone) is at far less a disadvantage than an Eastern (Eastern Time Zone) team playing at a Western (Pacific Time Zone) team.

After reading that article, I made some adjustments to the "Tired NBA Eastern Road 'Dogs in the West" system, excluding matches between teams in the same time zones for example, but the research uncovered a fact that was even more interesting. Games involving teams traveling west don't have as many points scored as the market expects. In a league where the talk is all about the increase in scoring, finding an edge on Unders was promising to say the least. 

Here are the results from backing the Under when Eastern Time Zone teams headed west for a game in a different time zone for the past ten seasons:

As the prior would suggest, the edge is stronger in the Pacific time zone than in the Central, with the Mountain zone hosting relatively few games. 

Using Joseph Buchdahl's spreadsheet, the chance that the results in the Pacific time zone are luck is 1 in 106. I share this idea because I'm a generous chap, and by the time the 2019-20 season rolls around, you'll all have forgotten about it! 

And for what it's worth, I use the previous season's average points total (adjusted) as my entry point because not all Unders are value.

Thursday, 13 June 2019

Take The Money Or Run?

In baseball betting, the question of whether it is better to bet on the Run Line or the Money Line is often asked, including on my Twitter timeline yesterday.

It's the kind of question that keeps me awake at night. To clarify the difference, to win a bet on the Run Line, the favourite needs to win by two or more runs, or the underdog lose by fewer than two runs.

It shouldn't be a surprise that road (away) teams win a higher percentage of games on the Run Line than home teams, because if the home team takes a lead in the bottom of the ninth or an extra inning, the game is over.

The road team doesn't have the luxury of knowing what is required, and so they will keep trying to pad any lead.

The statistics show this quite clearly - from a robust sample size of 37,905 matches, the percentage of wins for road teams covering the Run Line is 75.4%, while for home teams, it is only 68.1%.

But of course, the market knows this, and the Run Line price is derived not only from the Money Line price, but also from the venue. 

The table below shows the approximate Run Line prices for some of the more common Money Lines, broken down by Home and Away and by league. 

The evens Run Line bet for a Home team falls around the PW = 0.675 mark, while for the Away team, it's around 0.62. 


But this chart is an average guide for MLB overall. The sharper minds among you are probably asking yourselves, "but isn't there a difference between the numbers for the two leagues?" and you would be correct. The Designated Hitter rule applied to games in American League ballparks, of course influences those numbers.

For example, the Run Line price on a -200 Money Line favourite (1.5) will average 2.087 if it is a National League team playing at home, to 1.8 if it is an American League team playing away. 

Then of course you need to look at whether they are playing away in a National League ballpark or an American League one. Averages only take you so far.  

One of the baseball ideas I've shared in this blog is the T-Bone system, and the results for Money Line and Run Line from 2011 to yesterday are:
Note that these profits are calculated based on risking the line to win one unit when playing on favorites, and risking one unit to win the line when playing on dogs.

While this is the basic system, it's always a good idea to look below the surface. For example, while most games are intra-league, since 1997 a number of games each season have been inter-league. From 214 games in that first season, the total has steadily increased so that since 2013, 300 games are now played, i.e. each team plays 20 such matches, the majority of which are played in June.

National League teams playing under American League rules have an ROI of -5.2%, while American League teams playing under National League rules are +2.0%.   

Looking at 'hotties', which have been a value bet since 2014, for home teams the ML ROI is 5.2%, while the RL 6.0%, but on the road, the percentages are both 9.9%

The public tends to favour home teams and be nervous of hot favourites in baseball, and other sports too, so the results aren't a huge surprise. That the inefficiency persists for so long, is.  

Oh My Yosh!

Although I pretty much ignore the murky world of tipsters, this story of Darren Rovell appeared on my timeline and might be of interest to some of you familiar with the Action Network

Darren Rovell, the former ESPN sports business reporter who currently works for the Action Network, a subscriber-based sports gambling information website, found himself under intense scrutiny after gambling aficionados on Twitter posted evidence he’d edited his bets after they’d been placed. And those bets were originally larger by many orders of magnitude than his usual bet sizes.
The importance of measuring the success of a tipster or system against level stakes is highlighted, in particular by the use of "yoshing", a term I hadn't heard before, although the strategy wasn't new.
Why would a gambler place bets like these? According to one bettor knowledgeable about sports wagering who agreed to speak with me on the condition of anonymity, the practice of suddenly and randomly increasing bet sizing by several orders is commonly referred to as “yoshing.”
When a sports tout is facing a losing record at the end of a sports season, there’s not much downside in placing wild bets that, if they pay out, give the appearance of a positive year overall.
“If you win, great, you can claim you finished the season a winner,” the bettor said. “If you lose, who cares? You were already in the red.”
As noted above, Rovell denied he was yoshing at the end of the college basketball season, but rather blamed the error on his unfamiliarity with certain aspects of gambling.
To be taken seriously, anyone claiming success needs their results to be verifiable. This is easy enough for pre-off bets, since there are published Closing Odds or sports databases available, but it's a challenge for those who make claims about being profitable on in-play betting.

Aside from the time needed for the latter, there's no guarantee that money will be available at a value price (if you're using the exchange) or that the bet will be accepted (if using a sportsbook). 

If you can make money betting in-play, then good for you, but because it is essentially anecdotal, and unverifiable, it's really not something worth sharing.

Puck Review 2018-19

Another market inefficiency paid dividends last night with the conclusion of the 2019-19 NHL season, and the Stanley Cup being won for the first time by the St Louis Blues.  

When a series goes to a Game 7, the public bias is to favour the home team, and with the higher profile of the game, they appear to back up this bias with money, which leads to an opportunity as A Lucky A Day stated back in January of 2018:

The only improvement would be taking out the opening words of "I think".

The NHL re-organised in 2013, and since then there have been 42 game 7s. Admittedly not a huge number, 
but the road team has won 22 of those games at an average price of 2.284 for a 17.9% ROI.

Over the same time period, the ROI for this strategy in the NBA is 8.4%, while in the MLB it is 29%, which sounds great, but the playoffs there are a different format, and there have only been eight game 7s.

Looking back to the 2004 season, which is as far back as the database goes, the ROI is 9.7% from the 17 matches.

We could have another Game 7 in the NBA if the Golden State Warriors win their last ever game at Oracle Arena tonight.  

Back to Ice Hockey and while the NHL Regular Season was excellent, the post-season playoffs were terrible.

The above basic system was mentioned in October, in a post that looked at the claim that early season favourites were undervalued. I didn't find any evidence of this with the favourites my system uses. 

Skeptics won't be budged, but the evidence to me is clear that the market is inefficient in these games. If you'd started backing these selections in 2016 after seeing the previous three seasons show great promise, the statistics for the past three seasons are:
If you're a risk taker and had taken the plunge after two seasons the 1-in-x probability becomes 1336:
This is a system I'll be playing again next season.  

Tuesday, 11 June 2019

Segunda Finishes Second

Spain's Segunda División wrapped up at the weekend, finishing as mentioned previously, just behind Serie B, at least in terms of the Draw strike rate. 

As with Serie B, this league is another one where the Draw is perennially a relatively big hitter. For the past six seasons, the strike rates are:
For comparison, the EPL has not had a season in that range since 2010-11. Like its Serie B cousin, this league also has few matches where the Draw is at 4.0 or higher, fewer than 7% last season. 

The Draw was priced as favourite, or joint favourite in 16 matches, even more than Serie B's 10 matches, and was priced at 3.0 or shorter in 115 matches. ore evidence if needed, that this is a different world to the EPL, where in 7,220 matches, the Draw has never been favourite.  

Backing the Draw in 'close' matches had an ROI of 6.64%, and as with Serie B, the return in these matches where the home team is favoured is higher than when the away side is favoured.

Serie B and the Segunda División were the only two leagues of the 17 I follow where the Draw occurred in 30% or more matches. 

My original plan was to review each league in a separate post, but with other things going on in life right now, I'm not sure I can justify the time, at least not this month. Hopefully I'll have something before the new season starts which will give you a good shot at being profitable on Draw betting in 2019-20.

If anyone has any leagues they would like me to look at sooner, let me know. Choose from the list is on the left and I'll do my best.  

Sunday, 9 June 2019

Serie B - Draw Central

Last night's results from Spain mean that for the third consecutive season, the league with the highest Draw percentage is Serie B - or at least, it is top of the leagues that I cover.

Spain's Segunda División is messed up this year, with the decision in January to expel Reus Deportiu, but award 1-0 wins to their opponents in all subsequent matches. 

These unplayed matches might count in the table, but they all need to be ignored from any analysis, as was the awarded Bolton Wanderers v Reading match in the Championship this season.

Only one draw yesterday means that even if all three remaining fixtures finish all-square today, Serie B's 32.75% strike rate cannot be surpassed. 

Serie B has a long history with the Draw, so no one should be surprised. 

Of course, a high percentage of Draws doesn't mean that backing them blindly is automatically a profitable strategy, but it does mean that it might be a good area in which to start looking. 

In 2016-17, fewer than 7% of matches closed with the Draw priced at 4.0 or higher. For comparison, 41% of English Premier League matches see the Draw at this price.

Backing the Draw in 'close' games in Serie B, resulted in an ROI of 9.7% from 107 selections.

For 'close', I'm using matches where the 'true' difference between the two teams is 25% or less.

In 2017-18, this number climbed to a little under 12% of matches, and backing the Draw in 'close' games, resulted in an ROI of 24.8% from 105 selections.

Did this profitability continue in 2018-19? In short, yes. This past season saw fewer than 6% of matches with the Draw priced at 4.0 or higher, and in 'close' games, the ROI was 11.4% from 94 selections.   

In the seven years for which we have Pinnacle data, it does appear that the market now shows a clear bias in favor of the Home team. From 2012-16, the edge was on backing the Draw when the Away team was favoured, but in the past three seasons, it's better to back the Draw when the Home team is favoured, a 29.4% ROI.

Saturday, 8 June 2019

Avoiding Games in the Big Apple

For those of you wondering where I had gone, my wife's grandfather passed away at the end of last month in California (age 96, and playing golf twice a week up to a few months ago so not too bad a life), which meant some last minute travel and unfortunately missing the Champions League Final. 

Rather inconveniently, his funeral was scheduled for an hour before kick-off. Very poor timing, but by all accounts, the game was terrible, so perhaps I didn't miss too much.

The timing may have been intentional as, like most older Americans, he was not a football fan. His one and only live football match was the 1994 World Cup Final in Los Angeles for which his dual Grammy Award winning son snagged a couple of tickets, and it amused me to hear him describe that afternoon as "probably the most boring of his life." 

Each to their own I guess. Being of rich Italian ancestry, I can't say it was the happiest afternoon of my life, but it certainly wan't boring! 

I am however, a little concerned that my wife may be a closet Liverpool or Tottenham fan, as ultimately she claimed to be too ill to attend the funeral in person herself, and sent me off on my own!

For those following, here are the May updates for some of the MLB systems I've mentioned, starting with the perennially successful T-Bone System (left).


The basic version had a 19-7 record, and an ROI to level stakes of 19.8%, or 16.6% if using the American standard of betting the line to win one point.


The month included a 13 game winning run and over two weeks without a loss, so a profit on the month is hardly surprising.

For the season to date, the record is 39-19 with the level stakes ROI at 7.8% or 5.6% for American staking.

The Overs method I generously shared last month also had a profitable May, with a record of 26-19-2, and an ROI of 12.2%.

For those backing hot favourites, losses from the rare losing start to the season were recovered in May, and results for the Money Line are below:
Some of you may be familiar with the concept of 'public teams', something to be aware of when betting on US sports. I read an article back in 2009 which included this nugget:
The Yankees continue to be THE public team in Major League Baseball betting. And much like Notre Dame in college football, the Dallas Cowboys in pro football, or Duke in college basketball, I've found that it's easier to, for the most part, just stay away from betting on or against these teams. However, after tracking New York again this spring it's time to call a spade a spade. And it's time to start betting against the Yankees blind.
To say it mildly, it wasn't the best of predictions, as the spade turned out to be a diamond, and the New York Yankees went on to win the World Series that year. 

If you had blindly backed them in every game, including post-season, you would have had an ROI of 6.4%, but the comment did make an interesting point about 'popular' teams, which is one to consider when looking to improve a basic system.

When the public bets on a name, rather then a number, there's an opportunity.  

For the five plus seasons above (2014-2019), backing the New York Yankees at home when hot favourites, would have lost you 15.60 points. 

This may not sound a lot out of a system that is up 208.38 points overall, but eliminating losing propositions can make a big difference to that all important ROI. 

Unsurprisingly, the numbers suggest that the market especially overreacts when the game follows a Yankees win over the same opponent. Recency bias anyone?  

And in case you were wondering, New York's other team, the Mets, are the absolute worst team to back as hot favourites at home over this period, costing another 17.95 points.

Thursday, 30 May 2019

Rationale Thinking

Most of my systems have sound logical reasoning behind them, including the Totals system mentioned yesterday for Major League Baseball. 

The system takes advantage of the fact that most punters have preconceived, and fortunately for us, incorrect ideas about what total might represent a good bet.

To use extremes to make the point, a total set at 14 would appear to the average punter as a high number, triggering the instinctive and quite natural response that "the value must be on Unders", while a total set at 5 would prompt the opposite reaction.

My thought process, as was the case with NBA totals, was that in some scenarios, a high total is value for an Overs bet, and likewise a low total offers value on the Unders. 

Note the "some scenarios". A blind backing of Unders below, or Overs above, any number won't work. Those scenarios included various factors such as the rules for each league, and the number of at-bats per game. 

This was the a priori (as philosophers and betting skeptics refer to it) behind this idea.

The important thing is that there was a logical reason why this system should work, and as it has turned out, as least so far, it does. 

Will the market correct? As with other ideas I've shared, it should, but it often takes its time in doing so.

Look at my College Football (Small Road Dogs) 50/50 system (left) which has had a winning record for 18 seasons straight.

It could be luck of course, as skeptics might have it.

Or.... Or it might just be that favourites are over-rated by the average college football bettor. Surely not. Everyone knows home advantage is worth three points. Maybe not always.  

But at what point does even the biggest skeptic become a convert? If 18 years doesn't do it, then probably 25 years wouldn't. I'm skeptical by nature myself, but I'm also open to a logical idea supported by years of evidence.

A Lucky A Day raises the possibility that the Totals System might be the result of data mining. To me, data mining is an exercise that produces systems such as:
Back the Home Favourite in an American League game when the pitcher lost last time out, the visiting team won their last game but gave up two runs or more in the third, the temperature was 73F or hotter, and the game is being played on a Thursday afternoon. 
(Incidentally, the day of the week can be significant, not because the result is impacted, but because the markets change, as has often been noted on betting forums regarding weekend horse racing).

Here's an actual example of a nonsense 'finding':
99 pitches or more, or the price was 162-plus, and they are 0-7 SU. Maybe. 

To me, this kind of thing (I hesitate to call them systems), is the result of data mining, but I'd argue that it's not data mining when there is rationale involved, rationale defined as:
a set of reasons or a logical basis for a course of action or a particular belief.
In this case, I have a theory about where and why totals markets might be inefficient and then see if the results show that to be the case. So far, they tend to do just that, especially if A Lucky A Day's 1 in 10,000 estimate is accurate!

Remember too that we're not looking for wins, but profits. We are looking for inefficiencies in the market. Find value, and the profits will come.   

Wednesday, 29 May 2019

MLB Totals Simplified

The @PinnacleSports Twitter account highlighted a 2016 article on totals betting in Baseball, with the following quote highlighted:

"At most bookmakers it is effectively impossible to beat the Over because unsophisticated bettors tend to go Over not realizing that the numbers don’t add up the natural way."
By extension, if the market is so efficient that you can't win on Overs, then you can't win on Unders either, but it's not that clear cut. The article itself includes this comment:
Even at Pinnacle we can’t be sure that we have the best of it, especially when the person betting is sharp, so often we have to move the line and offer what we think is likely to be value. While the under is more likely to offer you value on a game where you don’t particularly like your side – especially if you’re playing at another bookmaker than Pinnacle – the over is where you can have vastly the best of it in the right spot. It’s never a sure thing that a pitcher will get it done.
As is often the case, the article complicates what is really quite a simple problem to solve. Let the market do its stuff, and take advantage of its inefficiencies.

I apply a similar strategy in baseball to that for the the NBA. When the total is (relatively) high, my expectation is that the public will be deterred from backing Overs, and Overs can thus offer value. 

Similarly, when the total is (relatively) low, the Under doesn't appeal to the unsophisticated public, and this selection can offer value. Remember:
Most punters lose. They are ill-informed, intrinsically lazy, psychologically flawed, impulsive, ill-disciplined, incapable of appreciating the importance of affect to the decision making process and prone to imitative and repetitive behaviour.
MLB has two conferences, and two sets of rules, with significant differences which need to be taken into account, and games hosted by the Colorado Rockies always have a higher total, something else that needs to be treated as an exception. 

The average total of runs per game over the past five seasons (including the current 2019 season) is 8.3 in the National League, 8.6 in the American League, and 11.0 for games in Denver.

The Totals markets are, at first sight, remarkably accurate.
For the National League, Overs and Unders are split as shown above, and the American League is close to 50/50 at 50.8% in favour of Unders.

First sight can be misleading however. 
  By adding a few conditions to my Totals bets, for example with Overs, I want the favourite to be the away team, (since that increases the chances of both teams batting all nine innings and 54 at-bats is much more favourable than 51 when you're wanting runs) it's not hard to see where the market weaknesses are. 

I reverse the rules for the Unders, and as you can see below, this is also a profitable strategy.

Results for the latest five seasons:

It's a simple, and extremely time effective, strategy. There may be sophisticated models out there, requiring a lot more effort, and requiring huge databases for evaluating pitchers and batting line-ups, but how many of them can match or exceed the double digit ROI percentage on Overs since 2015? 

Likely not many. Simple is often best - the KISS principle.

Sunday, 19 May 2019

Cassini's Law

There's a law known as "Cassini's Law", which states that the moment you start talking about a long winning sequence, along comes a loser. Or in this case two.

The T-Bone System ended its winning run at 13, a number not renowned for being associated with good fortune, with not one, but two losses on Friday, which takes the profit on the basic systems down to 9.25 and 8.60 points on the Straight Up and Run Line respectively.

Results for May to level stakes so far:

Meanwhile in the NBA Playoffs, the strategy of backing Unders is profitable so far. 

The logic I use here is that in the playoffs, the total in Eastern Conference games over the last ten seasons is six points fewer per game than in the regular seas, while in the Western Conference, the drop in points is 2.51. 

However, weighting so that recent seasons are more impactful, and the averages are 4.61 and 1.58 points per game.

This season, the regular season average total for Eastern Conference games was 219.7 points, so backing the Unders in games where the line is around 215 points or higher should be unpopular with the public, and thus offer value to the sharper bettor. Likewise in the West, the total where the squares balk should be around 222 points.

So far, we have a 10-7 record in the East, while in the Western Conference, we have a 5-2 record. A 15-9 record is a 22% ROI, but with so few matches in the playoffs, be wary of some variance with this idea.     

And with that, I leave you for a week. I'm off to Las Vegas shortly at work's expense for a different type of conference, but hoping to find a few hours to play some Craps and watch some games. Craps may have a -EV, (1.36% or less if playing optimally and taking the odds), but it's a lot of fun, a very social game, and after a big win a few years ago, I'm still in lifetime profit. Not to mention the free drinks, meals and rooms they will offer you after a long session.

A hot roll at the Craps table is like a winning sequence on a sports system. You want to keep pressing, but know at some point the 7 will show up.