Opening Day
It is here. Though it won’t be for long. The very gusts and chills that precede it now, will issue this joy farewell one day. Those days seem far off, especially while these are so near. The evenings of warm, familiar voices beckoning sleep as the bullpen grows thin; the afternoons of the crack and cheer, riding the breeze, echoing through open windows, are not far off now.
There are narratives, as there always are; new faces, old names, foreign locations, and legacies growing stronger. They don’t need descriptions or specifics for a mental image to be conjured. We know them. We feel them. They live in our hearts.
How does this game, this cruel, twisted, brutal, merciless, impossibly difficult game captivate us with such repetitive, maddening routine? Because it’s our game. It’s the game that comes back on the wings of new life, and leaves on the groans of a dying year. The game is life. It bears new, fantastic and exciting fruit every year. It returns luster to the old forest, which it seems has always been there. It begins with a grin and a cheer long buried beneath snow. It ends with tears and reverence for what seems like a lifetime of strife for one magical, heroic moment.
The game is different for all, just like life. It can be examined by a microscope, run through statistical models, skimmed in the paper with coffee in hand, or simply enjoyed on an afternoon off, letting the sun fade that old cap.
We need not know precisely why and how this long love affair goes on, because it always will. It’s not a number or a theory. It’s how it makes us feel. It gives us the greatest joy and sorrow, often only moments apart. It sets the stage for heroes and lines the walls with legends. It crafts curses which don’t live in storybooks and can really be broken. It gives hope to those who only need a ball to feel it.
It is here. Though it won’t be for long.
Welcome back baseball, we’ll see you tomorrow.
- Jake Reagan
Fun With Numbers
Back in 2013, I had the pleasure of working as a statistical consultant with my former high school baseball coach as part of an internship for college credit. My coach was a math teacher by day, passionate about the game, and always looking for that next level of analysis that might give him an edge on the competition. Getting to intern with him was a match made in heaven.
Coach had a rather fascinating blend of old-school and new-school philosophies, that successfully came together time and time again during his tenure at my former school. During that season, he enlisted my help to devise statistical models that graded out his players based off of tracking stats most people don’t even think to observe. After putting our heads together, the brainchild that emerged was the Productive At Bat (PAB for short).
The PAB was truly a fascinating stat. The purpose of this stat was to figure out which players made the most use out of their plate appearances, regardless of the outcome, and then build the lineup in a way to maximize productivity. In order to do this, we tried to eliminate as many variables as we reasonably could that fell outside of the players’ control when they were up to bat. Players could be credited with a PAB after all kinds of outcomes, even strikeouts, provided certain conditions were met.
In order to be credited with a PAB, players had to meet at least 1 of the following conditions…
Reach base
Advance a runner
Hit a line drive
See 5+ pitches
Make the defense earn the out/hitter was robbed
Obviously, reaching base and advancing runners are the two main objectives of any offense. This essentially incorporates the concept of On Base Percentage, while including situations where “productive outs” are made.
Including the condition for line drives allows us to remove some of the elements of “bad luck”. The idea behind this was that you wanted players who were hitting the ball well getting more at bats, so we simply disregarded the outcome of the at bat if they struck the ball well (for the purpose of this stat).
After covering the basics, we started to go more in depth and try to think of ways a player could have a positive impact on the game with an at bat that didn't already meet the existing criteria that we had. Pitches seen seemed like the perfect fit. We settled on 5 as the minimum number because, if every player took at least 5 pitches per at bat, a starting pitcher with a perfect game would still hit 85 pitches before they made it through 6 innings. In high school, pitchers are restricted to no more than 85 pitches before they are required to come out. If we could average 5 pitches per at bat, we could grind down even the most elite opponents, and have a chance to win against their bullpen. No matter what happened after 5 pitches, the batter would have at least done their part to tax the opposing pitching staff.
That last condition was a really interesting one. While it would be impossible to use the stat league-wide with that condition (since it would be impossible to know if coaches were all in agreement on when the defense earned the out, or the hitter was robbed), that final condition allowed for Coach to reward his players when he felt they got cheated by the umps, or were the victims of some stellar defense.
The end result was a very simple metric that told you what percentage of at bats a player made good use of.
ODDS TO WIN THE NL CY YOUNG
11/15/2018 - 2018 NL Cy Young - Odds to Win
Clayton Kershaw3-2In Progress
Max Scherzer2-1In Progress
Noah Syndergaard12-1In Progress
Madison Bumgarner15-1In Progress
Carlos Martinez15-1In Progress
Stephen Strasburg15-1In Progress
Yu Darvish18-1In Progress
Robbie Ray20-1In Progress
Jacob DeGrom20-1In Progress
Zach Greinke25-1In Progress
Jon Lester30-1In Progress
Jose Quintana30-1In Progress
Aaron Nola40-1In Progress
Jon Gray40-1In Progress
Kyle Hendricks40-1In Progress
Gio Gonzalez40-1In Progress
Chase Anderson50-1In Progress
Johnny Cueto50-1In Progress
Michael Wacha50-1In Progress
Julio Teheran50-1In Progress
Alex Wood50-1In Progress
Rich Hill50-1In Progress
Jason Vargas60-1In Progress
Tyler Chatwood100-1In Progress
Kenley Jansen100-1In Progress
Zach Davies100-1In Progress
Taijuan Walker100-1In Progress
Kenta Maeda100-1In Progress
Matt Harvey100-1In Progress
Good Luck... Danny B
ODDS TO WIN AL CY YOUNG AWARD
11/15/2018 - 2018 AL Cy Young - Odds to Win
Chris Sale9-4In Progress
Corey Kluber5-2In Progress
Justin Verlander8-1In Progress
Carlos Carrasco8-1In Progress
Luis Severino10-1In Progress
James Paxton12-1In Progress
Dallas Keuchel15-1In Progress
Gerrit Cole18-1In Progress
Masahiro Tanaka20-1In Progress
David Price25-1In Progress
Chris Archer28-1In Progress
Cole Hamels30-1In Progress
Jose Berrios30-1In Progress
Shohei Ohtani30-1In Progress
Aaron Sanchez40-1In Progress
Garrett Richards40-1In Progress
Marcus Stroman40-1In Progress
Danny Duffy40-1In Progress
Lance McCullers60-1In Progress
Michael Fulmer60-1In Progress
Sonny Gray60-1In Progress
Felix Hernandez80-1In Progress
JA Happ80-1In Progress
Rick Porcello80-1In Progress
Ervin Santana100-1In Progress
Craig Kimbrel100-1In Progress
Trevor Bauer100-1In Progress
Drew Pomeranz100-1In Progress
The Idiot’s Guide to Baseball Projections: By Nate Rawlings
SECTION I: NEWTON’S LAW
Patterns are an inevitable, inescapable even, fact of life. If the patterns of people are carefully observed, certain outcomes can be quite accurately anticipated (and often gambled on), even before they occur.
When it comes to baseball, much of the same logic applies. Sometimes, what our “gut” is telling us about the team we follow is merely a subconscious observation of a pattern we don’t even know we caught onto. Much of what I do revolves around unearthing patterns in the game and finding (or creating) stats that illustrate what our guts haven't been able to put into words for so long.
With respect to any sort of statistical model, the K.I.S.S. method (Keep It Simple, Stupid) is always ideal. The more extraneous or unnecessary variables one introduces, the more potential points of failure there are for your model to go wrong. Nothing gets introduced unless the results of its inclusion can justify its use.
Projection models are always an interesting pursuit, since the “best” design doesn’t always yield the most correct results. Every season doesn’t always boil down to the most predictable (and statistically likely) result unfolding. The top overall seed doesn't win every March Madness Tournament, the favorite in Vegas doesn't win every Super Bowl, and the most statistically probable outcome doesn’t always unfold every baseball season. However, unlike some sports with much shorter seasons (that allow for statistically unlikely results to have a much greater impact on the season), Major League Baseball’s 162-game slate gives a large enough sample-size that the end result will have a much higher probability of falling within an expected range.
Newton's First Law states that, “An object at rest tends to remain at rest, and an object in motion tends to remain in motion, unless it is acted on by some other force.”
Generally, this law holds true, not just with objects in the vacuum of space, but across a broad array of topics in day to day life. A broke college kid will remain broke until, a gracious employer overlooks the fact that the kid doesn’t have the preferred 10 years of experience for an entry level position and hires them anyway.
In baseball, a playoff bound franchise is likely to remain playoff bound, and a losing franchise is likely to continue with its losing ways, until they are acted upon by some other force (i.e. an injury, trade, player development/decline, etc.).
So, when it comes to projecting this season’s results, the best place to start is by simply taking the results from last season. If this year were to occur in a vacuum where the results were immune to the impact of aging, unforeseen injuries, and offseason player movement, the outcome would simply be a repeat of last season. It’s as simple as saying if this year’s results equal “X”, and last year’s results equal “Y”, then X = Y.
When examining actual results in the win/loss column compared with a team’s performance, it doesn't take an original stat or mind-numbing algorithm to let the average fan know that the results aren't always an accurate reflection of a team’s overall effort. The first adjustment we can apply is to change last season’s win total to one that more accurately reflects overall performance. To win a game, a ball club must score more runs than they allow their opponent to score. A simple way to compare actual wins to expected wins is by examining a team’s run-differential.
SECTION II: EXAMPLES FROM LAST SEASON
Lets examine a real life example from last season. In the following chart, we can see the run differentials of 4 unidentified teams.
Now, If I were to tell you that one of these teams finished with a record that was nearly .500 (80-82) which one would you pick? Do you go with Team A, which has scored about as many runs as it has given up, or do you go with another team that scored significantly fewer runs than they gave up?
If you guessed Team A, you’d be correct!
However, the results do not always wind up being so cut and dry. As you are about to find out, they can get deceptively misleading.
What if I were to tell you that a second team ALSO finished with the same record as Team A, which one would you pick? Every other team on this list gave up far more runs than Team A, while scoring significantly fewer runs.
Furthermore, what if I told you the remaining two teams finished 10 games apart in the standings, in spite of near-identical run differentials?
As bizarre as this all sounds, this is exactly what happened last season for the Angels, Royals, Blue Jays, and Phillies.
Before we lose all faith in the ability of run differential to accurately reflect (and project) wins for a team, let’s not forget that the Angels and the Blue Jays have records that correlate with their run differential. As for the other two teams, the Royals and the Phillies dealt
with several mid-season variables that significantly impacted their team’s trajectories, leading to skewed results. Teams like the 2017 Royals and Phillies are why I am painstakingly going over all of this. While the superficial results make sense for half of these teams, there is a deeper layer of analysis that, when uncovered, help us to make sense of the remaining results (as well as learn what to look for to predict future outcomes moving forward).
The Royals faded down the stretch and dealt with their top starter, Dan Duffy, missing a quarter of the season (making only 24 out of 32 starts). The Royals had a similar run total to the Angels, who won 80 games, but yielded 82 more runs. This can easily be accounted for by having the best pitcher on the staff for only 75% of the season, with replacement-level pitching yielding a disproportionate number of runs relative to the total losses accrued by the team. Not to mention, the Royals also dealt with losing their star catcher just a week after the trade deadline.
The Phillies shipped out one of their top 3 starting pitchers at the August 1st trade deadline, along with one of their most statistically productive position players. The team was also dealing with two of their top 3 remaining starters being injured.
An inconsistent presence of solid contributors on offense and defense can easily skew a team’s win total from resembling the total of a different team that put up nearly identical offensive and defensive totals.
These variables can create volatility, and ocular inaccuracy in a team’s overall results. The variables lead to teams being streaky, and can drastically skew large enough sample sizes that they can even significantly alter the outcome of the entire season. While it is insultingly simplistic to assume that a team’s year-to-year production (and results) will be similar, even if significant contributors were limited due to injury or being traded, the overall volatility of production is often constant. Injuries are ever present, especially among certain players. Different teams deploy different strategies and styles in their front offices that influence volatility as well. If we can spot a pattern between teams that consistently yield superior results with inferior production, or yield inferior results with superior production, we can apply that anticipated skew in results on a team specific basis.
SECTION III: MATH
So, to recap, we have now taken our projections from simply being…
X = Y
(X is this year, Y is last year)
to
X = y
(lower case ‘y’ is the adjusted wins of last year)
to
X = y(V)
(‘V’ is a volatility coefficient that reflects a trend in skewed results for a specific team)
The formula has now taken into account team production, and ability to capitalize on that production, in making projections from one year to the next. However, two very obvious variables still remain unaddressed. The first is significant player transactions made in the offseason. The second, is time.
Addressing roster turnover is simple enough. Simply cut and paste past production of new players in and remove existing production of former players, while adjusting for any change in roles between their new and former teams. For example, if a player goes from being a starting pitcher on their former team to slotting in as a long reliever on their new one, it would be inaccurate to credit their new team with 150-200 innings of production out of a guy who is likely only going to throw between 60-100 innings in the coming season.
As for the effects of time, I will write a separate piece later that examines the many ways to account for aging in anticipating player growth or decline. For now, let’s simply acknowledge it’s impact for the purpose of this narrative.
We have now gone from…
X = y(V)
to
X = y(V) + Pn - Pf
(Pn represents new players, Pf represents former players)
to
X = y(V) + R(Pn) - Pf
(R represents the change in roles of new and existing players)
to
X = [ y(V) + R(Pn) - Pf ]^T
(T represents the impact of time on the entire projection as a whole)
My apologies to anyone who had repressed nightmares from high school algebra dredged up by the depictions of this whole process in a step-by-step formula, but there is a reason mathematicians use them. I wanted a concise way to sum up everything covered here in a way that illustrated how all these variables related to each other for the reader. Stay tuned for when I apply what we discussed here to analyze and project the upcoming season!
Baseball : The Art Of War By Nate Rawlings
The Art of W.A.R.
In this ever modernizing era of baseball, we have found ourselves inundated with a plethora of advanced metrics. These metrics, unlike the common “counting stats” that can easily be followed, are increasingly obscure and difficult for the average fan to follow. However, one centralizing statistic that has emerged to the forefront is Wins Above Replacement, WAR for short. WAR, in theory, is a statisticians dream. A singular, reliable unit of measurement that gives us common ground to compare the value offered by every different kind of player. However, multiple, self-appointed “authorities” on the stat have shoved their way to the front, and each offers a different statistic, but with the same name. As a statistically-inclined baseball fan, I was both intrigued by the concept of WAR as it has emerged over this past decade, as well as abhorred by how much the stat itself seemed to lack direction. It already is a disservice to the game to have multiple “authorities” pushing their own interpretation of “WAR” out there to the consumers. Adding insult to injury is the fact that the average fan doesn't have the slightest clue of where to begin with learning to comprehend how a box score line for a player translates into the accruement of that given player’s WAR. Player A goes 1-4 with a homer and Player B goes 4-4 with 4 singles. Player A is a designated hitter with no defensive value, but player B made an error in an underwhelming performance on defense. Who had the better game? Who accrued the most WAR? Fans are trying to embrace the ever-modernizing statistics of today’s game, but there’s been somewhat of a disconnect for them when it comes to WAR, since they are just told to blindly accept some algorithm that spits out a value of 5 for Player A and 4 for Player B, so Player A must be worth 1 more win than Player B, even though there’s no explanation of how that value came to be arrived at. This is where I’d like to introduce my personal “one size fits all” stat for measuring the production of players, Net Bases.
I began working on trying to develop my own universal metric for evaluating baseball players back in 2009 when I was a freshman in college. I was working as a statistical consultant with my school’s baseball team, and I initially was curious at trying to determine WAR values for the many players on our team. I consulted the internet, and immediately was met with a bunch of noise about what the definition was, but no quantitative interpretations of how those numbers were arrived at. As soon as I realized there were multiple different “authorities” who peddled their interpretation of the stat, I closed my browser window and decided to create one myself. I did not wish to pry any further and risk tainting my personal bias when approaching this problem. I wanted my solution to be uniquely my own, untainted by what I would find if I dug deeper. However, I quickly found myself grappling with the most basic, fundamental concept there was with respect to this subject. What the hell is a win, exactly, and how do I quantify it?
Realizing that this was a problem to be saved for later (seven years later, as it would turn out) I opted to break it down into a more simple concept instead. While I could not fully quantify what a win was, I definitely knew how a win was achieved. Runs. As long as Team X has a greater number of runs than Team Y at the end of the game, Team X will win. Ok, so I was going with runs, that was easy enough. How do I determine how many runs its worth when a center fielder does his job catching a routine fly ball? Or when a leadoff hitter singles, steals, but gets stranded? Or two different players go 0/4 in a game, but one struck out all 4 times, while the other managed to put the ball in play and forced the defense to do it’s job to keep him off the bases. I was quickly falling down the same rabbit hole, until the obvious became apparent to me. Bases. It takes 4 bases to score a run, and it is a lot easier for both myself, as well as the typical fan who isn't a total stat nerd, to follow along with bases over runs. That leadoff hitter who singled and stole second accrued 2 total bases. Thats worth half a run, even though he didn't score. While saying thats worth half a run leaves some fans scratching their heads, virtually everyone who can achieve being conscious with a pulse can understand that two bases were achieved, since the leadoff hitter made it to second. While Net Bases as a whole is nowhere near as simplistic as that explanation is, it boils down every play of influence a given player has on the game into how that player’s performance influenced his team’s ability to generate total bases, and prevent their opponent from doing the same. While the world may not agree with it, this approach, in my opinion, seemed far more practical. Wins can vary, teams can win games they do not deserve to win, and lose games where they gave forth a far more winning effort. Wins are fickle. Runs are Runs, and there are 4 bases in every run.
Nate Rawlings
Odds To Win The PAC 12
3/10/2018 - NCAA Mens Basketball - PAC 12 Tournament - Odds to Win
Arizona8-5In Progress
USC8-2In Progress
UCLA9-2In Progress
Arizona St11-2In Progress
Utah11-2In Progress
Oregon11-2In Progress
Stanford15-1In Progress
Washington30-1In Progress
Oregon St30-1In Progress
Colorado50-1In Progress
Washington St300-1In Progress
California500-1In Progress
Good Luck... Danny B
Odds To Win The Big East
3/10/2018 - NCAA Mens Basketball - Big East Tournament - Odds to Win
Villanova4-7In Progress
Xavier10-4In Progress
Creighton6-1In Progress
Butler7-1In Progress
Seton Hall10-1In Progress
Marquette35-1In Progress
Providence35-1In Progress
St Johns35-1In Progress
Georgetown100-1In Progress
DePaul200-1In Progress
Good Luck.. Danny B
Odds To Win The BIG 12 Tournament
3/10/2018 - NCAA Mens Basketball - Big 12 Tournament - Odds to Win
Kansas9-5In Progress
West Virginia5-2In Progress
Texas Tech3-1In Progress
TCU7-1In Progress
Baylor12-1In Progress
Oklahoma18-1In Progress
Texas18-1In Progress
Kansas St18-1In Progress
Oklahoma St25-1In Progress
Iowa St200-1In Progress
Good Luck... Danny B
Odds To Win The NL East
Washington Nationals 1-4
New York Mets 10-2
Philadelphia Phillies 9-1
Atlanta Braves 25-1
Miami Marlins 200-1
Good Luck.... Danny B
Odds To Win The NL
Los Angeles Dodgers11-5In Progress
Chicago Cubs16-5In Progress
Washington Nationals8-2In Progress
St Louis Cardinals11-1In Progress
New York Mets15-1In Progress
Milwaukee Brewers15-1In Progress
San Francisco Giants15-1In Progress
Arizona Diamondbacks17-1In Progress
Colorado Rockies20-1In Progress
Philadelphia Phillies50-1In Progress
Pittsburgh Pirates55-1In Progress
Atlanta Braves100-1In Progress
San Diego Padres100-1In Progress
Cincinnati Reds150-1In Progress
Miami Marlins1000-1In Progress
Good Luck... Danny B
Pick Your Sweet 16 Teams
2018 Mens NCAA Basketball Tournament - Odds to Win
Michigan St9-2In Progress
Virginia10-2In Progress
Villanova10-2In Progress
Duke10-2In Progress
Kansas12-1In Progress
North Carolina12-1In Progress
Purdue14-1In Progress
Cincinnati20-1In Progress
Wichita St20-1In Progress
Xavier20-1In Progress
Ohio St22-1In Progress
West Virginia25-1In Progress
Gonzaga28-1In Progress
Auburn28-1In Progress
Michigan35-1In Progress
Arizona35-1In Progress
Tennessee40-1In Progress
Texas Tech40-1In Progress
Missouri45-1In Progress
Kentucky45-1In Progress
Oklahoma55-1In Progress
Florida70-1In Progress
NC State80-1In Progress
Clemson85-1In Progress
Rhode Island85-1In Progress
St Marys100-1In Progress
Florida St100-1In Progress
Seton Hall100-1In Progress
Houston125-1In Progress
Nevada125-1In Progress
Butler150-1In Progress
Creighton150-1In Progress
Who Will Hit The Most Home Runs In 2018
10/1/2018 - 2018 Most Regular Season Home Runs (All Bets Action) (Others Available on Request)
Giancarlo Stanton7-2In Progress
Aaron Judge6-1In Progress
Cody Bellinger12-1In Progress
Bryce Harper15-1In Progress
JD Martinez15-1In Progress
Nolan Arenado18-1In Progress
Mike Trout18-1In Progress
Joey Gallo20-1In Progress
Manny Machado25-1In Progress
Kris Bryant30-1In Progress
Josh Donaldson30-1In Progress
Chris Davis30-1In Progress
Freddie Freeman30-1In Progress
Paul Goldschmidt30-1In Progress
Rhys Hoskins40-1In Progress
Khris Davis40-1In Progress
Edwin Encarnacion40-1In Progress
Miguel Sano40-1In Progress
Nelson Cruz40-1In Progress
Kyle Schwarber50-1In Progress
Anthony Rizzo50-1In Progress
Mark Trumbo50-1In Progress
George Springer50-1In Progress
Carlos Correa50-1In Progress
Joey Votto60-1In Progress
Yoenis Cespedes80-1In Progress
Marcell Ozuna80-1In Progress
Logan Morrison80-1In Progress
Andrew McCutchen80-1In Progress
Adam Duvall80-1In Progress
Good Luck... Danny B
Eric Thames80-1In Progress
Gary Sanchez80-1In Progress
Odds To Win The NBA East
6/1/2018 - NBA Eastern Conference - Odds to Win
Cleveland Cavaliers5-9In Progress
Toronto Raptors6-2In Progress
Boston Celtics5-1In Progress
Washington Wizards20-1In Progress
Philadelphia 76ers30-1In Progress
Milwaukee Bucks30-1In Progress
Detroit Pistons79-1In Progress
Indiana Pacers100-1In Progress
Miami Heat200-1In Progress
Charlotte Hornets500-1In Progress
Brooklyn Nets5000-1In Progress
Chicago Bulls5000-1In Progress
New York Knicks5000-1In Progress
Atlanta Hawks5000-1In Progress
Orlando Magic5000-1In Progress
Good Luck... Danny B
Updated Baseball Odds To Win The WS
FutureOddsStatus
11/1/2018 - 2018 World Series Championship
New York Yankees5-1In Progress
Chicago Cubs5-1In Progress
Houston Astros7-1In Progress
Los Angeles Dodgers7-1In Progress
Cleveland Indians7-1In Progress
Washington Nationals16-2In Progress
Boston Red Sox14-1In Progress
Los Angeles Angels20-1In Progress
St Louis Cardinals22-1In Progress
New York Mets25-1In Progress
Toronto Blue Jays30-1In Progress
San Francisco Giants30-1In Progress
Minnesota Twins30-1In Progress
Milwaukee Brewers30-1In Progress
Colorado Rockies35-1In Progress
Arizona Diamondbacks35-1In Progress
Seattle Mariners60-1In Progress
Philadelphia Phillies100-1In Progress
Baltimore Orioles120-1In Progress
Tampa Bay Rays120-1In Progress
Pittsburgh Pirates120-1In Progress
Chicago White Sox200-1In Progress
Oakland Athletics220-1In Progress
Atlanta Braves220-1In Progress
San Diego Padres220-1In Progress
Texas Rangers220-1In Progress
Cincinnati Reds300-1In Progress
Miami Marlins500-1In Progress
Kansas City Royals500-1In Progress
Detroit Tigers500-1In Progress
Good Luck... Danny B
Odds To Win 2019 NFC Championship
1/20/2019 - 2019 NFC Championship - Odds to Win
Philadelphia Eagles5-1In Progress
Green Bay Packers6-1In Progress
Minnesota Vikings7-1In Progress
New Orleans Saints10-1In Progress
Atlanta Falcons10-1In Progress
Los Angeles Rams12-1In Progress
San Francisco 49ers12-1In Progress
Dallas Cowboys14-1In Progress
Seattle Seahawks15-1In Progress
Carolina Panthers18-1In Progress
Detroit Lions30-1In Progress
Tampa Bay Buccaneers30-1In Progress
New York Giants40-1In Progress
Washington Redskins40-1In Progress
Arizona Cardinals50-1In Progress
Chicago Bears50-1In Progress
Good Luck... Danny B
Odds To Win 2019 AFC Championship
1/20/2019 - 2019 AFC Championship - Odds to Win
New England Patriots12-5In Progress
Pittsburgh Steelers5-1In Progress
Jacksonville Jaguars10-1In Progress
Houston Texans10-1In Progress
Oakland Raiders13-1In Progress
Los Angeles Chargers14-1In Progress
Kansas City Chiefs15-1In Progress
Denver Broncos20-1In Progress
Baltimore Ravens20-1In Progress
Indianapolis Colts20-1In Progress
Tennessee Titans25-1In Progress
Miami Dolphins50-1In Progress
New York Jets50-1In Progress
Buffalo Bills50-1In Progress
Cincinnati Bengals50-1In Progress
Cleveland Browns75-1In Progress
Good Luck... Danny B
Golden State Huge Favorite To Repeat
6/15/2018 - 2017/18 NBA Championship - Odds to Win
Golden State Warriors20-40In Progress
Houston Rockets8-2In Progress
Cleveland Cavaliers8-1In Progress
Boston Celtics20-1In Progress
Oklahoma City Thunder25-1In Progress
Toronto Raptors25-1In Progress
San Antonio Spurs50-1In Progress
Washington Wizards75-1In Progress
Minnesota T-Wolves75-1In Progress
Philadelphia 76ers75-1In Progress
Milwaukee Bucks75-1In Progress
Detroit Pistons150-1In Progress
Miami Heat200-1In Progress
Indiana Pacers200-1In Progress
Portland Blazers500-1In Progress
Charlotte Hornets1000-1In Progress
Los Angeles Clippers1000-1In Progress
New Orleans Pelicans1000-1In Progress
Utah Jazz1000-1In Progress
Denver Nuggets1000-1In Progress
Memphis Grizzlies9999-1In Progress
Los Angeles Lakers9999-1In Progress
New York Knicks9999-1In Progress
Brooklyn Nets9999-1In Progress
Orlando Magic9999-1In Progress
Atlanta Hawks9999-1In Progress
Dallas Mavericks9999-1In Progress
Phoenix Suns9999-1In Progress
Chicago Bulls9999-1In Progress
Sacramento Kings9999-1In Progress
Good Luck... Danny B
Updated AL Odds
Houston Astros10-4In Progress
New York Yankees15-5In Progress
Cleveland Indians375-100In Progress
Boston Red Sox12-2In Progress
Los Angeles Angels10-1In Progress
Toronto Blue Jays14-1In Progress
Minnesota Twins20-1In Progress
Seattle Mariners25-1In Progress
Tampa Bay Rays55-1In Progress
Baltimore Orioles55-1In Progress
Oakland Athletics100-1In Progress
Chicago White Sox100-1In Progress
Texas Rangers110-1In Progress
Kansas City Royals250-1In Progress
Detroit Tigers250-1In Progress
Good Luck... Danny B