As I use only Sabermetric data, I need to ensure anyone reading or listening to my content understands what I am saying, why I use the data, and what everything means. I have a multitude of charts. These are compiled from specific data points from several places. My inability to write Python code means I manually input these every day. I was reminded to explain everything though. I will lay this out to match how my charts are laid out so the information will match the charts you are reading or I refer to.
WEBBIE ODDS
WEBBIE ODDS are the first to be published every morning. They are composed of a blend of FanGraphs, Baseball Prospectus, and BaseRuns projected standings. Each team has a probable winning percentage that is put into a formula that gives a probable winning percentage when these teams play each other. From the winning percentage, I use FanGraphs (I will abbreviate as FG going further) projection systems. They show Steamer and Zips projections for players. Both Steamer and Zips are companies that make projections. I use an average WAR for the pitchers to adjust the winning probability due to the strength of the pitchers. This generates a winning probability that needs to be adjusted for the home field and turned into a money line. For example, a winning percentage of 56.41% becomes -129.
WAR- Wins Above Replacement. It is the value for a player compared to an average player shown by how many more wins he is responsible for. The larger the number the better.
WIN Probability or Winning Percentage: The percentage a team will win when facing the opponent. Win probabilities can be turned into money lines by using an odds calculator. Here is a link for one: https://www.aceodds.com/bet-calculator/odds-converter.html
WEBBIE EV+ TOOL
WEBBIE ODDs are input along with market odds into the WEBBIE EV+ TOOL. They are shown as My Probability (WEBBIE ODDs) and Listed Odds. My Probability column populates the MY ODDS column. TRUE ODDS are the listed odds when the sportsbook vigorish is removed. The Implied No Vig% is the probability of true odds. The Implied w/VIG column is the probability of the Listed Odds. The vigorish column is the difference between the No Vig and with Vig column. The Value column is the one that shows EV+ situations. If the result is “black”, there is a positive expectation or more probability of winning than the sportsbook is offering. If it is “red”, there is less probability than the book is offering, and is a negative expectation.
EV+ = Extra Value Positive, The definition is when there is a greater probability of winning than what the sports book is offering.
Pitcher Report
The GSC sections refer to Game Score. Using game score, I can see how a pitcher’s past results are using a composite scoring system rather than a pitching line. GSC1 is the last start for the pitcher or the prior to the one he is making today. GSC3 is an average of a pitcher’s last three starts. GSC7 is an average of a pitcher’s last seven starts. The reason for seven, is during a 30-45 day stretch, a pitcher will start 6-8 times. I want to use his last 30 days as baraometer for his current form is. I look at his last 3 and last 1 to gause the rgression bar. If a pitcher has a GSc60 in the GSC3 column and a GSc52 in the GSC7 column, it means that he is overperforming his curent form and regreeion is due or ripe for this pitcher. GSC YTD is the games score ofr the season. By using game score, I can quickly asses the quality standard, and gauge whether this pitcher should throw above or below his standard in this game.
Game Score: Game Score was created by Bill James to measure the quality of individual starts. While most baseball fans are familiar with the traditional “pitching line” of Innings, Hits, Runs, Earned Runs, Walks, and Strikeouts, James’ Game Score consolidates those statistics into a single number that makes comparing starts easier.
xFIP and SIERA I refer to as metrics. I do not use ERA. I use these instead. When there is an “x” in front of any Sabermetric data point, it is a predictive data point. It is telling you what will happen not what has happened. That is exactly what we are looking for. These metrics tell quite the story of our pitchers. They have signigicant meaning to expectatins. To use xFIP, we must define FIP. FIP is Fielding Independent Pitching. It measures BBs, Ks, and HRs. It is only things the pitcher controls. I have used the term “Three True Outcomes”. Those three outcomes are BBs, K, and HRs! Think of it this way, this measures how much a pitcher is in need of his defense. Thes metrics are shown like an ERA so it is easy to know what is a good acore and what is a poor one. The lower the number th better. So xFIP, tells us how a pitcher will control the game from the mound and if his defense will be needed. I often use the terms balls in play to describe this. The more balls in play, the more the defense has to be involved.
SIERA is Skill Interactive ERA. While FIP and xFIP largely ignore balls in play — they focus on strikeouts, walks, and homeruns instead — SIERA adds in complexity in an attempt to more accurately model what makes a pitcher successful. SIERA doesn’t ignore balls in play, but attempts to explain why certain pitchers are more successful at limiting hits and preventing runs. This is the strength of SIERA; while it is only slightly more predictive than xFIP, SIERA tells us more about the how and why of pitching.
Here’s what SIERA tells us:
Strikeouts are good…even better than FIP suggests. High strikeout pitchers generate weaker contact, which means they allow fewer hits (AKA have lower BABIPs) and have lower homerun rates.
Walks are bad…but not that bad if you don’t allow many of them. Walks don’t hurt low-walk pitcher nearly as much as they hurt other pitchers, since low-walk pitchers can limit further baserunners. Similarly, if a pitcher allows a large amount of baserunners, they are more likely to allow a high percentage of those baserunners to score.
Balls in play are complicated. In general, groundballs go for hits more often than flyballs (although they don’t result in extra base hits as often). But the higher a pitcher’s groundball rate, the easier it is for their defense to turn those ground balls into outs. In other words, a pitcher with a 55% groundball rate will have a lower BABIP on grounders than a pitcher with a 45% groundball rate. And if a pitcher walks a large number of batters and also has a high groundball rate, their double-play rate will be higher as well.
xWOBA and L30 xWOBA
wOBA is weighted on base average. Weighted On-Base Average (wOBA) is one of the most important and popular catch-all offensive statistics. wOBA is based on a simple concept: Not all hits are created equal. Batting average assumes that they are. On-base percentage does too, but does one better by including other ways of reaching base such as walking or being hit by a pitch. Slugging percentage weights hits, but not accurately (Is a double worth twice as much as a single?) and again ignores other ways of reaching base. Weighted On-Base Average combines all the different aspects of hitting into one metric, weighting each of them in proportion to their actual run value. While batting average, on-base percentage, and slugging percentage fall short in accuracy and scope, wOBA measures and captures offensive value more accurately and comprehensively. xWOBA is the expected weighted on base average. It is shown to look like a btting average. However, move the expectation up a little nit. an average WOBA is .325.
While xWOBA is an offensive data point, I get to use it to define how batters will do against the pitcher. xWOBA vs OPP is how batters who have batted against this pitcher are expected to perform. It is pitcher versus hitter. xWOBA L30 is how the pitcher has performed over his last 30 days. I get to see what an offense is going to do against this pitcher before the game!
K/9 is strikeouts per nine innngs. My chart is red and green. Green is good and red is bad. A pitcher over 8.50 K/9 will be green. The goal is to limit baserunners and I want to know how many are eliminated via striekouts.
BB/9 is walks per nine innings. Just like above, this one though is less than 3.00 to be green. You might her me say or read my writing where i say ” he puts guys on base”. This is what I mean. We want to know how hard an offens has to work to get on base, and a pitcher puts them on base, they have an easier time with base runners, which in turn, generate scoring chances.
STUFF+ and PITCHING+
These are pitching metrics that measure the quality of pitches thrown. It is on a 100 scale so a 112 is 12% better than average. STUFF+ measures the characteristcs of pitches, including release point, velocity, vertical and horizontal movement, and spin rate. The higher this number, the better a pitcher’s arsenal is to get batters out. PITCHER+ combines STUFF+ with location and batter handedness. It show how a pitcher uses his arsenal to get outs.
AVG and WHIP
AVG is the batting average for hitters against the pitcher and WHIP is Walks plus Hits divided by innins pitched. WHIP tells us how many baserunners per inning a pitcher allows. The goal is to gauge these metrics against league average. Is the pitcher better or worse? Again green or red on the charts. We get know how baserunners per inning to expect. Keep in mind that baserunners equal scoring chances.
GB%, BABIP and BABIP Variance
GB% is groundball percentage. This tells us in comparison to league average if a pitcher gets more or less groundballs. A pitcher will want these, but not all pitcher are groundball guys. Their pitch plane and velocity will generate launch angles and we want to know what to expect. Groundballs are less damaging than flyballs.
BABIP is batting average on balls in play. By itself, BABIP really means nothing. What is tells us is on balls that are put in play, how many of these are hits. A pitcher does not control this entriely, he just effects it. When BABIP is compared to league average, we can see if a pitcher is unlucky or less favorable that balls in play against him have not found fielders. This also means that it will regress to the mean, thus he will start getting outs where he wasn’t before. I measure this through the BABIP Variance column. If the metrics is red, and BABIP is yellow, the pitcher has been fortunate and need to be cautious of ball becoming hits. If the metric is green, then the pitcher has been unlucky and his fortune should change to having balls become outs instead of hits.
HARD HIT RATE
Hard hit rate is the measure by percentage of the type of contact a batte makes. The harder a ball is hit, the more likely it will go for a hit and it has a greater chance of being a damaging hit.
WIN Probabilty Added
Like in football wher epa is a big deal, WPA is the same thing. It measures every play in terms of how much value was added to win the game and to lose the game. WPA is the total calcualtion of positive plays less the negative plays. This metric builds upon itself over the course of plays. I only use the data from the past 30 days. Another way to look at this, is when things get tough, does the pitcher respond? A positive number does indicate a “bulldog” mentality to winning games because he get those positive outcomes to help his team win. A negative score shows a lack of toughness which favores the batters.
OFF WAR
OFF WAR = Offensive WAR. WAR is measued by offense plu defense. When I am trying to get the value of battrer offensively, I use the OFF WAR metric. It is WAR minus defense. This tells us how many wins over a replacement player a hitters have contrubuted. I use only 7 day batting stats. Any time a player is listed with a 2.0+ OFF WAR, his name gets listed into the cluster section of teh Offense Report. Everyone with a 1.0+ plus result gets added to the strip on the right. I also in clude the negative -1.0 and above. The yellow column is the total of positive and negive OFF WAR results showing the cluster.
ISO=Isolated Power. ISO measures extra base hits as percetage aginst non extra base hits. Isolated Power (ISO) is a measure of a hitter’s raw power and tells you how often a player hits for extra bases. We know that not all hits are created equally and ISO provides you with a quick tool for determining the degree to which a given hitter provides extra base hits as opposed to singles. ISO tells you the average number of extra bases a player gets per at bat and this is a piece of information you want to know. ISO saves you steps from average and slugging because it is a cobination of them in data point. I measure team offense via OFF WAR and ISO. It is helpful to how teams score and ISO helps identify those variances.
For anyone not using my data or tools, I post them daily on my website. I will be glad to explain anything futher if necssary (www.webbie20mlb@gmail.com). I would also invite you to become a memer and recieve this kind of data daily. However,. it will come with my analysis and picks.
Ust this link to become a member: https://mlb-daily.com/payment-block-media-and-text/