Most Predictable Sport
Sports Betting Guide
Football and basketball are the most predictable. The worst teams practically never beat the best teams. In many years there's a college football team that goes undefeated, and the best NFL team is usually 14-2 or something like that. The best college basketball team usually wins around 90% of its games, and the best NBA team 80%. Predictable Mumbai City FC messing it up when it matters the most. Ritabrata Banerjee. Twitter facebook reddit copy. Comments (0) ISL.
Football or basketball? Tennis or maybe hockey? You often ask us which sports disciplines are the most predictable and thus the easiest to bet on. It is hard to give a simple answer to that question. Nevertheless, after years of analysing sports betting, we have observed a few tendencies when it comes to particular sports disciplines. In this article, we will analyse some of the most popular sports with a special focus on their predictability.
Is betting on football a lottery?
Let us begin with the world’s most popular sport - football. A sport which is also favoured by the majority of sports punters. From mathematical point of view football does not seem to be very predictable. Why? First of all, taking into consideration the fact that there are three possible outcomes of every match: victory, draw and defeat, we have only 33,3% chances for correct prediction of a football match result with regular bets and 50% chances with two-way bets, especially asian handicaps.
- Soccer has 3 very probable outcomes. It's a mathematical loss right at the beginning. It's also a team sport, hence a lot of information is needed to compensate for those 33%. Some people still argue that soccer is the most predictable sport, those people are nut jobs.
- Basketball seems to be more consistent than soccer or hockey, which makes it great for things like under/over betting. One downside is that you can usually only bet on win or loss (no tie). Depending on who you ask, you'll always get different opinions on what sport is the most predictable one. Please log inor registerto add a comment.
Furthermore, we are constantly reminded by the media that football is getting more and more corrupted, and the possibility of fixed matches is increasing. In a way, football became a victim of its own popularity. Remember that the favourite may not need to lose, for the scammers to win money in a fixed game. Sometimes, all it takes is for the underdog to score a goal, for example, the first one in the game. The odds for such possibilities are often very high, and nobody will remember that the team who eventually won 4:1, conceded the first goal. Nobody, except people who made millions on that game. We believe that the possibility of making big, easy money is too tempting for some people, and such situations will, unfortunately, happen.
As for the most prestigious top European competitions, we think they are mostly free of corruption. Millions of fans around the world watch the best European teams compete each week. Every single match is being recorded and live-streamed with the usage of state-of-the-art technology and multiple cameras. This leads to match-fixing in such leagues being extremely difficult, although not impossible. Keep in mind that there are leagues in which we can be confident that corruption is very unlikely due to recently imposed restrictions and laws of football federations.
What is more, in competitions such as UEFA Champions League, English Premier League, Spanish La Liga, German Bundesliga, French Ligue 1 or Italian Serie A, the financial level of clubs as well as their reputation is at such a high level, that they cannot afford to damage their public image over some corruption scandal. This was visible a few years ago in the Calciopoli scandal where few Italian teams, Juventus among others, were punished and even relegated to second division because of the corruption. The club from Turin, as well as Italian football in general, had to rebuild its name from the beginning.
Without a doubt, competitions that are more exposed to corruption are those from national leagues and cups of post-socialist countries such as Ukraine, Poland, Lithuania, Latvia, Georgia and even Russia as well as Balkan states including Serbia, Macedonia, Bosnia and Herzegovina, Montenegro, Albania and Kosovo. If you have decided to put your money on football betting, you should pay attention to the moment of the season. Nowadays, nobody is surprised by the weird games at the beginning and especially at the end of the season, where theoretically stronger teams often lose points to weaker sides that are in need of those. What is more, two clubs who have very good relations will sometimes ‘give’ win or a point in the match to the side that desperately needs it. When betting on football, we should consider even the smallest details like that.
Summing up, the lower and more obscure the competition is, the less predictable it is and even with proper knowledge of statistics and good reflexes you could still lose money while betting on such events due to corruption and numerous variables that you are not aware of due to the lack of information.
Ice-hockey predictability
Hockey is another sports discipline which is worth considering to bet at. This sport increasingly gains popularity among both fans and punters. Unfortunately, for hockey punters, the chances of successful prediction in hockey are even lower than in football. Online bookmakers are aware of this and thus offer higher odds for hockey matches, even in cases of those with a clear favourite. The main reason for that is to encourage punters to place bets on this discipline.
It should be noted that games of two strongest and most popular hockey leagues, NHL and KHL are the hardest to predict. In the NHL (Canada and U.S.A), which is considered to be the best hockey competition in the world, surprising results are very common, and even the best teams often lose to complete outsiders. This could be explained by the number of games in the season, as well as the level of the players of each team in those leagues.
The problem of the Kontinental Hockey League is the finances which are often influenced by the Russian mafia, as well as the political situation of the region. The standings in this mostly Russian league change very rapidly and heavily depend on the status of the companies that sponsor the majority of hockey clubs in KHL. Unfortunately, we cannot say that this competition is free of corruption, as it’s stated otherwise on many occasions. It is much easier to predict the outcomes in other European leagues, especially Sweden, Finland, Czech Republic, Slovakia, Germany and Switzerland. The reason behind this may be that there is much less money invested in those leagues, which consequently decreases the possibility of a fixed game.
Predictability in tennis betting
Tennis is considered to be one of the most entertaining sports in the world. Both men and women competitions are quite popular among fans. Once again, we do not have any good news for punters, as this sport is also very unpredictable. However this can be used as an advantage too, as in the case of other more unpredictable sports, you can bet against the favourites in hopes of winning a lot of money because of the high odds. It should be noted though, that it is very hard for an inexperienced punter without knowledge and intuition to find such an opportunity.
Why is tennis such an unpredictable sport? There are a few reasons. When it comes to strictly sport aspects, remember that tennis is mostly an individual sport, in which a lot depends on the players’ shape on a particular day. In contrast to team sports, tennis players are on their own. The main problem for tennis players is inconsistency, which is especially visible in women’s competitions.
Apart from that, tennis games are really easy targets for all sorts of frauds and scams that are connected with game-fixing. The number of games and tournaments is so high that both journalists, punters or even fans, are not 100% certain that a given player will play in a particular event. It is especially important when it comes to less prestigious competitions, with smaller prize pools and awards. Top tennis players often tend to skip such events to rest before the more important competitions.
Similarly to other sport disciplines, for a game to be ‘fixed’, one of the players does not have to lose. As we already mentioned that in a football section, all the player needs to do is to lose a point, in this case, a set or few gems, so that the scammers will win millions. How often do we see a tennis match between a clear favourite and the underdog, where the better player takes control over the majority of the game and then, at some point, starts fading. In this case, the odds for the weaker player to win even a single set are astronomical. It is a perfect situation for fraud. The better player will win the game in the end, it does not matter that he lost a few points on the way. Nothing seems suspicious at first sight, doesn’t it?
Another important thing that is worth remembering is that more experienced tennis players can easily control the game. There are tennis matches, where you know the skill difference between the players after only a few exchanges. A better player can use this to his/her advantage, and if he is corrupted, he can lose some points along the way. Nobody will suspect anything, after all, mistakes happen to even the best.
High predictability of basketball
Many of you will be surprised, but in comparison to other disciplines, we consider basketball to be quite a predictable sport. There are several strong arguments for this thesis. First of all, most basketball bets are two-way bets, not regular three-way like in other sports. Our chances increase from 33% up to 50% because of this. Furthermore, odds for basketball games, are known for being relatively decent, and there is a wide variety of different types of bets. An experienced punter may even find a few value bets. If someone has considerable knowledge about particular teams, their strengths and weaknesses as well as their recent performances, he has good chances of successfully predicting the outcome of a given basketball game.
Another thing that makes basketball easier to predict is its points system. Unlike football or hockey, basketball games are known to have big scores because of its intensity and scoring (you can score more than one point in a single action). There are games where you can definitely expect lots of points. What is more, in basketball, every player scores points. Even more, defensive-minded blocking players tend to score a lot in the game. If a punter uses his/her knowledge of the particular teams and players and their scoring statistics, he could win money by successfully betting on Over/Under bets and handicaps.
Furthermore, because of this intensity that we mention, the scores tend to change very rapidly and often a team can win the game in a matter of seconds despite being a few points down. Therefore, if a punter has good reflexes and a bit of luck, he can live-bet on the team that is losing at the moment, knowing they have their momentum and could win the game in a few minutes. You can win a lot of money that way because of live-betting, the odds for the teams that are losing are much higher. If you are more adventurous, you can also try to predict the winning margin of the game.
Another important factor is the schedule. In basketball, there are a lot more games than in tennis or even football. Thus, the players tend to be very tired after a few consecutive games. This is a perfect opportunity for betting. What is more, you should also pick teams that are very good at home. Unfortunately, there are only a few teams that use their home-advantage very good. There are also teams that tend to score more in away games. If they play a team that is not as strong at home, it could be a great chance for placing an Over bet or simply Money Line bet on the away team.
You should also remember that the NBA, the most popular basketball competition in the world, differs from other basketball leagues like, for example, EuroLeague and national (domestic) European leagues. Read this if you want to know more about the predictability of basketball in the NBA and Europe. It should be noted that the NBA is much more balanced and more unpredictable when it comes to results in comparison to other competitions such as the EuroLeague.
Predictability of other sports
Volleyball predictability
Although volleyball is considered to be quite predictable, it is one of the least attractive team sports to bet on. It is probably because of the low number of people who bet on it, as well as often insufficient knowledge of the bookmakers about the competitions, teams and players. As for the predictability of volleyball matches, it is very obvious that stronger teams rarely lose with the weaker ones. For years, the same clubs and nations have dominated the sport. That is why the bookmakers are offering handicaps and special bets to attract punters.
Handball predictability
Similarly to volleyball, handball is thought to be one of the less popular team sports. Both punters and fans prefer other sports such as football or basketball. First of all, handball is not very attractive because of its low predictability. Surprising results happen quite often in both club and national competitions. In the cases of other sports, this could be an advantage, here there is no successful system or strategy, and long-term betting on handball proves not to be profitable at all. What is more, as in the case of volleyball, the bookmakers offer low odds because of the small knowledge about the sport. The last factor behind lower predictability of handball is the classic three-way types of bet that are traditionally used to place a wage.
Cricket predictability
Despite being one of the most popular disciplines of sport in the world, cricket is not the punters favourite. Most bets on this sport are placed by the cricket fans and very experienced punters who have found a value bet in a particular cricket event. As for crickets predictability, most experts agree that with very few exceptions, both club and national cricket is quite predictable. This results in lower odds for the favourites such as England or India. It should be said here that club competitions, especially in lower, more obscure leagues are much less predictable than national competitions such as the Cricket World Cup.
Rugby predictability
Like cricket, rugby is one of the most widespread sports on the globe. It is especially popular in Great Britain and its former dominions. As in the case of already mentioned cricket, rugby in all its varieties is not as popular among punters as its younger brother - American football. When it comes to predictability in rugby, it is very hard to state whether the results in rugby are surprising or not. Often, the underdog can win with a better team, but usually, the same countries and clubs take over competitions.
Horse-racing predictability
Even though horse-racing is considered to be one of the oldest sports of our civilisation, it is not as well-known as other sports. It is different when it comes to betting on horse-racing as both sports betting and bookmaking were created mainly because of it. Furthermore, the majority of betting terminology was created for horse-racing betting. With time, the popularity of horse-betting diminished, but it is still relatively strong, especially in England. Bookmakers offer various bets for this sport, with Tote betting being the most popular form of horse-race betting. Most experts agree that horse-racing is not a very predictable sport. It is mainly caused by the fact that in this sport, the most important factor is the horse, its health, shape and even breed. Sometimes, even the best horses lose the race because of an injury or the smallest detail that distracted them.
Speedway predictability
The most important factor in speedway is motorbikes. The type and the quality of the bike is crucial for the final outcome of the race. Even the best driver will not succeed without a good machine. Because of that dependency, speedway is considered to be quite unpredictable. Even with proper analysis and considerable knowledge about this sport, it is often very hard to predict the winner of the race, not mentioning about the correct order of places of all participants. Defects, crashes and numerous variables that could happen during the race that can completely change the final result.
Another important factor is the driver. His technique, mentality and experience have a significant impact on his performance in the race. There are roughly about 30-40 top speedway drivers in the world so analysing their statistics and performances will take significantly less time than analysing a single football team. The key to successful speedway betting is to know the hierarchy of the competitions both for clubs and nations. They differ in prestige and money prizes. The bookmakers offer lower odds for the competitions that are more obscure and which they do not have much information about. It should be said that speedway is not considered a popular sport among both fans or punters, even in the category of motorsports, it is far behind NASCAR, Formula 1 and MotoGP.
Boyd's World->Breadcrumbs Back to Omaha->Predictability across Different Sports | About the author, Boyd Nation |
Publication Date: April 8, 2003
Why Are We Here And Not Over There?
There are lots of reasons why we end up as a fan of a specific sport (and, no, I'm not arguing that you're limited to just one, but most of us do tendto specialize at some point). A lot of them are aesthetic -- I lovedbasketball as a player, and it televises well, but give me a choice betweensitting in a gym in December or sitting out in a baseball stadium in May, and it's not a hard choice. Some of them have to do with our own physicalcharacteristics -- baseball and hockey require pretty good eyes at timesto follow well. Many times it just comes down to some formative memorythat builds a fire. For most folks, they don't even think about why theylike a sport, it just clicks with them.
For a lot of these folks, some of this can be traced back to thepredictability of a sport -- how likely am I to know the end result whenthe game starts? Those who like a safe outcome, with just a dash of upsetthrown in to keep the mix from getting too bland, tend to become footballfans. Those who like to see merit rewarded but like a good bit of unpredictability tend to become baseball fans.
For this study, I've pulled together comprehensive score data for the lastfive years for a number of sports, and I want to answer three questions foreach of them. The first addresses the points above -- how predictable isthe sport? For that, I'm looking at this question: Given two teams whodiffer by a given amount in quality, as measured by the ISR's, which seemto work pretty well with all the sports given here with one caveat, howlikely is the weaker team to win? To look at this, I looked at the range ofquality within the sport -- how wide is the range between the best team andthe worst team? I also looked at the probability functions for each sport,similar to the 2% per ISR point rule of thumb that works pretty well forcollege baseball (in other words, a team with a 10-point ISR advantage willwin 70% of the time, ignoring the home field advantage), but couldn't finda good way to present that in an understandable manner; maybe some othertime.
The second question concerns the notion of competitive balance -- howlikely is it that a team will be about as good one year as they were theyear before? For that, I'm comparing ISR's from year to year with acorrelation measure. Finally, I'm curious about how well the postseason isset up for the sport. In other words, how good are their champions? I'vedone this for eleven sports or variants of sports, and some of thecomparisons are interesting. I'd love to add soccer, softball, orvolleyball (or any other team sport you can point me to data for), butthese are the only ones I've been able to find sufficient scores for yet.
College Baseball
Average Range: 62.1 - 126.5
Competitive Balance: .87
Champions: 1/7/3/4/1/286
We'll start here, with the sport we know best (for those of you who arefans of other sports and got here through Google or whatever, read thisanyway) and explain the different measures.
The range is the average of the lows and the highs in absolute ISR measuresfor the five seasons. I don't discuss absolute ISR values very much(they're designed to balance around 100 and form a nice normal curve overthe sport), but they're useful here to show the magnitudes of relativequality in each sport. The tighter the range, the closer the worst andbest teams in the sport are, and the more likely an upset is in any givengame. College baseball is the most competitive (or most random) of thecollege sports.
The competitive balance measure listed here is the result of correlatingthe ISR value from one year to the next for all teams that played insuccessive seasons in the sport. .87 means that a team that successfulin one season is quite likely to be successful in the next.
On the championship line, the first five numbers represent the ISR rank ofthe national champion. If your goal for the postseason is to find out whothe best team is, 1 is good here. If you're a fan of 'the excitement ofupsets', higher is better, I suppose. For all its faults, the collegebaseball postseason has produced some fairly good national champions, butit turns out that it's unusual for a team lower than about #3 in its sportto win a title, so Miami's 1999 championship is still off the charts. Thelast number on the line is the number of teams who competed in 2002.
Major League Baseball
Average Range: 93.3 - 107.9
Competitive Balance: .55
Champions: 1/6/9/4/4/30
For all the complaints about 'faith and hope' (and the anti-trustexemption, which means we get to see these lies told directly to Congress),we have here the most highly competitive sport of them all. There's amoderate correlation between success from year to year (which is actuallygood, since you want well-run teams to continue to succeed and poorly-runteams to be forced to make changes), but in any given game, there's verylittle difference between the best and the worst. We also have the mostpoorly-designed postseason I can find.
Men's College Basketball
Average Range: 69.4 - 131.6
Competitive Balance: .79
Champions: 1/2/2/1/2/322
The range is actually closer than I expected, and the competitive balanceis at least a little more prone to volatility than college baseball. Forall the jokes about being a fan of, say, Vanderbilt football, it appearsthat there's actually little less likely to be rewarding than being a fanof a bad college baseball team (with the exception of college hockey, to benoted later).
For all the CBS chest-thumping about the unpredictability of March Madness,the postseason does a remarkable job of picking a team that's at least veryclose to being the best. The only team outside the top 10 in the last fiveyears to reach the title game is Indiana in 2002; two years of the lastfive have featured #1 and #2 playing for the title.
Women's College Basketball
Average Range: 65.3 - 139.9
Competitive Balance: .84
Champions: 1/2/1/3/1/324
As the budgets shrink a bit from the men and the limelight fades a little,things get a bit less competitive; as much as there are fabled programs inmen's basketball, there's really nothing there that compares with Tennesseeor Connecticut women's basketball.
NBA
Average Range: 83.9 - 113.3
Competitive Balance: .69
Champions: 1/1/1/1/1/29
The less random nature of basketball relative to baseball helps out the NBAtremendously in the PR wars against MLB (although less so than awillingness not to repeatedly denigrate their own product). Thecompetitive balance correlation is higher than in baseball by quite a bit,but nobody's complaining in August that certain NBA teams have no chance tomake the playoffs, because they can let over half the league into thepostseason and still manage to quite predictably have the league's bestteam win the title. Side note: Nobody does NBA pools, and gambling'sillegal in this state, but take San Antonio this year, although it's closerthan usual.
WNBA
Average Range: 86.4 - 117.2
Competitive Balance: .54
Champions: 1/1/1/1/1/16
Even if the nature of the sport is to encourage teams to stay good oncethey're good, new leagues are not a great proving ground for it, so I'mnot sure there's much to be concluded here.
College Football
Average Range: 47.4 - 132.7
Competitive Balance: .64
Champions: 1/1/3/1/1/152
As a minor side note, I'm not convinced that the ISR's are the best measuring tool for college football, since there are so few observationpoints in a season. The wide range is due to the fact that I don't havea source for scores that excludes 1AA teams. I'm actually somewhatsurprised that the top end is as low as it is; my impression (although Ihaven't followed football in over a decade) was that the best team almostalways won. I guess the relative rarity of undefeated teams would argueagainst that -- relatively speaking due to the number of games, the bestwomen's college basketball teams are actually more dominant than the bestcollege football teams.
For all the grousing about the lack of a real playoff, it's worth notingthat the best team almost always ends up as national champion; somethingthat's not as likely under any proposed playoff system.
NFL
Average Range: 81.6 - 116.4
Competitive Balance: .29
Champions: 1/4/1/4/1/32
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Here we have an interesting paradox -- a sport where, by professional standards, there's a fairly wide range between the best and worst teamsin any given year, but being good one year is only a slight indicatorthat a team will be good the next year. It will be interesting to seeif the change in the way of producing the schedule will change thesenumbers.
Men's College Hockey
Average Range: 42.4 - 140.4
Competitive Balance: .93
Champions: 2/2/2/2/132
I'm on thin ice here, so to speak, since I know almost nothing abouthockey, but this looks like a sport with a huge gap between the haves andthe havenots that's unlikely to change. There's nothing inherent in thepostseason structure that I can see that would keep the #2 team winning, sothat may just be one of those weird coincidences. I don't have data for1998.
Women's College Hockey
Average Range: 54.9 - 141.6
Competitive Balance: .92
Champions: NA/NA/NA/2/2/68
Remember how little I knew about hockey? Subtract some of that for women'shockey. As far as I can tell, there's only been a national championship fortwo years now, so there's not even much history to look at.
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NHL
Average Range: 88.4 - 109.3
Competitive Balance: .66
Champions: 2/1/4/1/1/30
We've got very competitive individual games, and a small but acceptableamount of turnover from year to year in the good teams, along with apostseason that produces a surprisingly accurate result, given the amountof randomness in individual games. So why are these guys going broke?
Pitch Count Watch
Rather than keep returning to the subject of pitch counts and pitcherusage in general too often for my main theme, I'm just going to run astandard feature down here where I point out potential problems; feelfree to stop reading above this if the subject doesn't interest you.This will just be a quick listing of questionable starts that havecaught my eye -- the general threshold for listing is 120 actual pitchesor 130 estimated, although short rest will also get a pitcher listed if I catch it. Don't blame me; I'm just the messenger.
Date | Team | Pitcher | Opponent | IP | H | R | ER | BB | SO | AB | BF | Pitches |
Mar 28 | North Carolina-Charlotte | Zachary Treadway | St. Louis | 9.0 | 9 | 3 | 2 | 5 | 8 | 34 | 42 | 129 |
Apr 4 | Campbell | Josh Blades | Central Florida | 7.2 | 12 | 4 | 4 | 5 | 4 | 32 | 38 | 147 |
Apr 4 | Mercer | Brandon Davidson | Jacksonville | 7.0 | 5 | 3 | 3 | 4 | 7 | 23 | 28 | 124 |
Apr 4 | Samford | Stephen Artz | Troy State | 8.0 | 13 | 5 | 5 | 3 | 6 | 35 | 39 | 147 (*) |
Apr 4 | Pacific | Matthew Pena | Cal State Fullerton | 7.0 | 14 | 10 | 9 | 4 | 2 | 33 | 39 | 134 |
Apr 4 | South Florida | Jon Uhl | East Carolina | 8.2 | 8 | 4 | 4 | 2 | 9 | 33 | 36 | 130 (*) |
Apr 4 | North Carolina-Charlotte | Zachary Treadway | Tulane | 8.1 | 9 | 6 | 5 | 3 | 7 | 33 | 38 | 122 |
Apr 4 | Houston | Brad Sullivan | Alabama-Birmingham | 5.2 | 4 | 3 | 3 | 6 | 5 | 19 | 27 | 124 |
Apr 4 | Ohio | Chris Bova | Marshall | 9.0 | 5 | 1 | 1 | 1 | 12 | 30 | 35 | 141 |
Apr 4 | Evansville | Tom Oldham | Creighton | 9.0 | 11 | 6 | 4 | 0 | 6 | 37 | 41 | 130 (*) |
Apr 4 | Alabama | Taylor Tankersley | Auburn | 4.1 | 9 | 7 | 7 | 3 | 7 | 23 | 28 | 125 |
Apr 4 | College of Charleston | Matt Soale | Davidson | 9.0 | 4 | 2 | 2 | 2 | 8 | 31 | 33 | 121 |
Apr 4 | Rice | Philip Humber | Hawaii | 8.0 | 6 | 3 | 3 | 1 | 11 | 29 | 31 | 125 |
Apr 5 | Missouri | Garrett Broshuis | Texas Tech | 7.2 | 8 | 4 | 1 | 2 | 5 | 31 | 34 | 126 |
Apr 5 | Cincinnati | B. J. Borsa | Southern Mississippi | 9.0 | 7 | 3 | 2 | 2 | 8 | 34 | 38 | 138 |
Apr 5 | Eastern Michigan | Anthony Tomey | Ball State | 7.0 | 4 | 1 | 1 | 5 | 9 | 26 | 31 | 129 |
Apr 5 | Eastern Michigan | Trevor Carpenter | Ball State | 6.0 | 8 | 5 | 4 | 1 | 7 | 27 | 28 | 128 |
Apr 5 | Miami, Ohio | Graham Taylor | Arizona | 9.0 | 10 | 4 | 4 | 4 | 6 | 35 | 39 | 155 |
Apr 5 | Ohio | Novosel | Marshall | 8.2 | 8 | 4 | 4 | 4 | 10 | 32 | 38 | 146 (*) |
Apr 5 | Evansville | Mitch Prout | Creighton | 9.0 | 6 | 3 | 3 | 5 | 7 | 30 | 40 | 144 (*) |
Apr 5 | Austin Peay State | D. Smith | Tennessee-Martin | 9.0 | 5 | 3 | 2 | 3 | 10 | 31 | 34 | 130 (*) |
Apr 5 | Murray State | Kyle Perry | Eastern Kentucky | 9.0 | 11 | 8 | 5 | 2 | 3 | 36 | 42 | 132 |
Apr 5 | Alabama | Johnson | Auburn | 7.0 | 5 | 1 | 1 | 2 | 9 | 25 | 27 | 120 |
Apr 5 | Kentucky | Heath Castle | Mississippi State | 9.0 | 7 | 2 | 2 | 3 | 5 | 31 | 34 | 132 |
Apr 6 | Texas | Justin Simmons | Baylor | 8.0 | 3 | 3 | 3 | 2 | 7 | 27 | 30 | 129 |
Apr 6 | Cal State Northridge | Leo Rosales | Long Beach State | 9.0 | 6 | 2 | 1 | 1 | 9 | 32 | 35 | 121 |
Apr 6 | William and Mary | Chris Shaver | James Madison | 8.2 | 9 | 3 | 3 | 4 | 5 | 29 | 35 | 122 |
Apr 6 | Texas-Pan American | Travis Parker | Texas A&M-Corpus Christi | 10.0 | 9 | 2 | 2 | 4 | 4 | 37 | 41 | 145 (*) |
Apr 6 | Texas A&M-Corpus Christi | Jimmy Hamon | Texas-Pan American | 9.0 | 6 | 2 | 1 | 4 | 11 | 32 | 38 | 148 (*) |
Apr 6 | Yale | Mike Elias | Princeton | 10.1 | 7 | 7 | 7 | 5 | 4 | 36 | 43 | 144 (*) |
Apr 6 | Evansville | Trevor Stocking | Creighton | 8.2 | 8 | 2 | 2 | 5 | 6 | 30 | 38 | 139 (*) |
Apr 6 | College of Charleston | Brett Harker | Davidson | 8.0 | 9 | 3 | 0 | 0 | 5 | 34 | 34 | 126 |
Apr 6 | Gonzaga | E. Clelland | Portland | 11.0 | 7 | 3 | 2 | 3 | 5 | 37 | 43 | 138 |
Apr 6 | Hawaii | Chris George | Rice | 6.2 | 7 | 6 | 6 | 6 | 5 | 26 | 32 | 123 |
Apr 7 | Washington State | Aaron MacKenzie | Stanford | 10.0 | 12 | 5 | 4 | 2 | 4 | 38 | 42 | 138 (*) |
Apr 8 | James Madison | Leatherwood | Richmond | 7.1 | 7 | 3 | 3 | 5 | 6 | 28 | 35 | 130 (*) |
Apr 8 | Georgetown | Salvitti | Maryland | 9.0 | 7 | 4 | 3 | 5 | 8 | 34 | 39 | 149 (*) |
Apr 8 | Texas-Pan American | Aaron Guerra | Texas | 6.1 | 8 | 7 | 4 | 7 | 5 | 26 | 37 | 139 |
Apr 9 | Southern Mississippi | Cliff Russum | Mississippi | 9.0 | 4 | 2 | 2 | 2 | 10 | 30 | 32 | 129 |
Apr 10 | Sacred Heart | Chuck Ristano | Long Island | 9.0 | 8 | 0 | 0 | 0 | 10 | 33 | 35 | 122 |
(*) Pitch count is estimated.
The Treadway line from the 28th is a correction based on an actual pitch count.
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Boyd's World->Breadcrumbs Back to Omaha->Predictability across Different Sports | About the author, Boyd Nation |