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New Stat - Baseball Prospectus: Deserved Runs Created

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  • New Stat - Baseball Prospectus: Deserved Runs Created

    https://www.baseballprospectus.com/n...45432/why-drc/

    First, all kudos to noticing this belong to layson27. He and I have been discussing the very favorable rating of one of my favorites, Earl Averill in Baseball Prospectus, but today layson27 noticed BP has this new stat.

    I thought I'd point it out to the forum, but layson27 is the one who found it first!
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  • #2
    Originally posted by Toledo Inquisition View Post
    https://www.baseballprospectus.com/n...45432/why-drc/

    First, all kudos to noticing this belong to layson27. He and I have been discussing the very favorable rating of one of my favorites, Earl Averill in Baseball Prospectus, but today layson27 noticed BP has this new stat.

    I thought I'd point it out to the forum, but layson27 is the one who found it first!
    Interesting and looks good for predicting the future of current players, but I am much more interested in accurate assessing the value of long retired players.
    .


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    • #3
      Originally posted by Toledo Inquisition View Post
      https://www.baseballprospectus.com/n...45432/why-drc/

      First, all kudos to noticing this belong to layson27. He and I have been discussing the very favorable rating of one of my favorites, Earl Averill in Baseball Prospectus, but today layson27 noticed BP has this new stat.

      I thought I'd point it out to the forum, but layson27 is the one who found it first!
      Thanks for sharing, another tool to assess : )
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      • #4
        Originally posted by SavoyBG View Post

        Interesting and looks good for predicting the future of current players, but I am much more interested in accurate assessing the value of long retired players.
        Most of the younger saber writers don't care much about pre 1980s baseball and history and they are more about predicting future.


        The stat looks interesting, so it basically regresses luck on BIP?
        I now have my own non commercial blog about training for batspeed and power using my training experience in baseball and track and field.

        Comment


        • #5
          Originally posted by dominik View Post

          Most of the younger saber writers don't care much about pre 1980s baseball and history and they are more about predicting future.

          Yeah, I know.
          .


          19th Century League Champion
          1900s League Champion
          1910s League Champion

          1930s League Division Winner
          1950s League Champion
          1960 Strat-O-Matic League Regular Season Winner
          1960s League Division Winner
          1970s League Champion
          1971 Strat-O-Matic League Runner Up
          1980s League Champion
          All Time Greats League Champion

          Comment


          • #6
            Like it or not...BBPro has gone full tilt predictive/skill based in their WARP with this and pitcher DRA. And applying it to players retroactively. It's a very iffy prospect.

            At least they are consistent now. FG is regresses luck for pitchers almost 100% (well, fip has a HR/fb component still included) and 0% for batters.

            But at least their catcher defense tools blow everyone else away.
            Last edited by Bothrops Atrox; 12-05-2018, 05:31 PM.
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            • #7
              It seems that in many respects, the difference between DRC+ and metrics like wRC+ or wOBA is considerably greater than the difference between the latter and traditional stats like BA and OBP: It even appears to be more reliable than Statcast metrics like xwOBA.

              https://www.baseballprospectus.com/n...-case-for-drc/

              If we may, let’s take a moment to reflect on the differences in performance we see in Table 3. It took baseball decades to reach consensus on the importance of OBP over AVG (worth five points of predictiveness), not to mention OPS (another five points), and finally to reach the existing standard metric, wOBA, in 2006. Over slightly more than a century, that represents an improvement of 12 points of predictiveness. Just over 10 years later, DRC+ now offers 13 points of improvement over wOBA alone.
              Also note:

              the changing baseball has wreaked havoc on traditional park-adjusted metrics. Thus, if you wish to use wRC+ or OPS+, particularly to study the last several baseball seasons, we recommend that you do so only with extreme caution.
              They provide a table of park factors, the range of which is noticeably shrunk compared to traditional factors. Thus while CO is still the highest, it's only at 104, not much higher than some other parks. And the lowest park factor, St. Louis, is about 98. They claim this results in large part because DRC+ uses only one year of data to calculate the factors. While this is a smaller sample size than several years, they claim that changes in the baseball necessitate it. These changes affect the run scoring environment in different parks differently.

              While I welcome this new stat, it comes with a loss of effective transparency. The formulas for metrics like wOBA, wRC+ or WAR are readily available, and anyone with not too much effort can confirm what an individual player's value is. I don't think it's going to be possible to do that with DRC+. You not only need a computer, but a particular program, to determine it.
              Last edited by Stolensingle; 12-09-2018, 08:49 PM.

              Comment


              • #8
                I have no doubt it does its job really well and is on point in terms of accuracy. Kind of like a great cup of coffee. Problem is, I don't drink coffee. Trying to figure out how I'd use it outside of predictive analysis.
                1885 1886 1926 1931 1934 1942 1944 1946 1964 1967 1982 2006 2011

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                Comment


                • #9
                  Originally posted by Bothrops Atrox View Post
                  I have no doubt it does its job really well and is on point in terms of accuracy. Kind of like a great cup of coffee. Problem is, I don't drink coffee. Trying to figure out how I'd use it outside of predictive analysis.
                  Well some people you fWAR for CY votings. Maybe in a couple years awards are given based on deserved stats (DRC, xWOBA) rather than what actually happened on the field.
                  I now have my own non commercial blog about training for batspeed and power using my training experience in baseball and track and field.

                  Comment


                  • #10
                    Originally posted by Bothrops Atrox View Post
                    I have no doubt it does its job really well and is on point in terms of accuracy. Kind of like a great cup of coffee. Problem is, I don't drink coffee. Trying to figure out how I'd use it outside of predictive analysis.
                    How do you use wRC+ or OPS+ outside of predictive analysis?

                    Comment


                    • #11
                      Originally posted by Stolensingle View Post

                      How do you use wRC+ or OPS+ outside of predictive analysis?
                      Those aren't based on hit expectancy. They are based on hit results. DRC+ is FIP for batters. And that is fine for predictive analysis. but just like ERA is a better indicator of long-term skill after a certain sample size, wRC+ is better than DRC+ too. Or I would imagine.

                      All I am saying is that I am not going to switch over to using DRC+ to evaluate careers. Not that I am saying BBPRo wants that either
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                      1887 1888 1928 1930 1943 1968 1985 1987 2004 2013

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                      • #12
                        Originally posted by dominik View Post

                        Well some people you fWAR for CY votings. Maybe in a couple years awards are given based on deserved stats (DRC, xWOBA) rather than what actually happened on the field.
                        As saberleaning as I am - I am totally against that.
                        1885 1886 1926 1931 1934 1942 1944 1946 1964 1967 1982 2006 2011

                        1887 1888 1928 1930 1943 1968 1985 1987 2004 2013

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                        Comment


                        • #13
                          Originally posted by Bothrops Atrox View Post

                          Those aren't based on hit expectancy. They are based on hit results. DRC+ is FIP for batters. And that is fine for predictive analysis. but just like ERA is a better indicator of long-term skill after a certain sample size, wRC+ is better than DRC+ too. Or I would imagine.
                          As far as I can tell, Judge and BBPro regard DRC+ as superior to wRC+ for any length of time. As explained in the link I provided, it's been found to be better not simply for prediction, but also description. They have also published a list of career DRC+ values as well as those for 2018, which wouldn't be of much use if they didn't think it was at least as good as wRC+.

                          Here are the top 10 DRC+ values for 2018 and career.. For comparison, wRC+ values are in (). Bold indicates a relatively large difference between the two values.

                          2018
                          Trout 180 (191)
                          Betts 174 (185)

                          Ramirez 162 (146)
                          Martinez 155 (170)
                          Arenado 146 (132)
                          Yelich 145 (166)
                          Bregman 143 (157)

                          Machado 143 (141)
                          Suarez 141 (135)
                          Carpenter 137 (138)

                          Career
                          Ruth 197 (197)
                          Williams 180 (188)
                          Bonds 175 (173)
                          Gehrig 168 (173)
                          McGwire 164 (157)
                          Foxx 159 (158)
                          Trout 155 (172)
                          Thomas 155 (154)
                          Mantle 154 (170)
                          Mize 152 (157)

                          https://www.baseballprospectus.com/d...-runs-created/

                          I’m not sure how the career values were calculated, since for players in older eras, all the relevant data would presumably not be available. But for 2018, note that most batters in the top 10 had significantly larger or smaller DRC+ values than wRC+ values. I think in most cases, this is probably explained in large part by the reduced range of park factors, as I mentioned in my previous post. Trout’s wRC+, for example, assumes he plays in a pitcher’s park, but that advantage is reduced in the single year data used for DRC+. The opposite is true for Arenado. His DRC+ gets a boost relative to his wRC+, because the park factor for Coors is found to be reduced.




                          Last edited by Stolensingle; 12-10-2018, 09:11 PM.

                          Comment


                          • #14
                            Originally posted by Stolensingle View Post

                            As far as I can tell, Judge and BBPro regard DRC+ as superior to wRC+ for any length of time. As explained in the link I provided, it's been found to be better not simply for prediction, but also description. They have also published a list of career DRC+ values as well as those for 2018, which wouldn't be of much use if they didn't think it was at least as good as wRC+.

                            Here are the top 10 DRC+ values for 2018 and career.. For comparison, wRC+ values are in (). Bold indicates a relatively large difference between the two values.

                            2018
                            Trout 180 (191)
                            Betts 174 (185)

                            Ramirez 162 (146)
                            Martinez 155 (170)
                            Arenado 146 (132)
                            Yelich 145 (166)
                            Bregman 143 (157)

                            Machado 143 (141)
                            Suarez 141 (135)
                            Carpenter 137 (138)

                            Career
                            Ruth 197 (197)
                            Williams 180 (188)
                            Bonds 175 (173)
                            Gehrig 168 (173)
                            McGwire 164 (157)
                            Foxx 159 (158)
                            Trout 155 (172)
                            Thomas 155 (154)
                            Mantle 154 (170)
                            Mize 152 (157)

                            https://www.baseballprospectus.com/d...-runs-created/

                            I’m not sure how the career values were calculated, since for players in older eras, all the relevant data would presumably not be available. But for 2018, note that most batters in the top 10 had significantly larger or smaller DRC+ values than wRC+ values. I think in most cases, this is probably explained in large part by the reduced range of park factors, as I mentioned in my previous post. Trout’s wRC+, for example, assumes he plays in a pitcher’s park, but that advantage is reduced in the single year data used for DRC+. The opposite is true for Arenado. His DRC+ gets a boost relative to his wRC+, because the park factor for Coors is found to be reduced.



                            Then maybe I am not getting exactly what it is using to come up with the values. I need to read the links again/more carefully.
                            1885 1886 1926 1931 1934 1942 1944 1946 1964 1967 1982 2006 2011

                            1887 1888 1928 1930 1943 1968 1985 1987 2004 2013

                            1996 2000 2001 2002 2005 2009 2012 2014 2015


                            The Top 100 Pitchers In MLB History
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                            Comment


                            • #15
                              Originally posted by Bothrops Atrox View Post

                              Then maybe I am not getting exactly what it is using to come up with the values. I need to read the links again/more carefully.

                              To be honest, I’ve not yet seen an article that explains it in detail. What I have seen, though, makes it clear that it’s determined by the same general approach used to determine DRA. This is considerably more complex than FIP, which one can calculate from traditional stats with nothing more than basic arithmetical skills.

                              For one thing, DRA takes into account more than a dozen factors, not just the defense behind the pitcher. It includes, e.g., the identity of the opposing batter, the catcher, the umpire, the base-out situation, the inning, the current score differential, even the temperature at the time the game starts. There are additional variables involving base stealing so that, e.g., a pitcher who allows relatively few stolen bases and/or relatively few stolen base attempts gets increased value. I assume that DRC, the basis of DRC+, takes into account similar factors (though not the base stealing ones), substituting the identity of the pitcher with that of the batter.

                              Second, DRA then combines all these factors, and some others, and regresses them. This is why a computer program is required. This is something you can’t do on a simple calculator. Even further, the values are modified by training them on data of previous seasons. That is, the effects of all these variables are used to predict runs allowed in the past, with the results compared with actual runs allowed. This comparison guides fine-tuning of the variables.

                              The question you’re asking or implying is, does all this manipulation provide a better description of a batter’s long-term productivity than just using linear weights, and assuming all the other events average out in the long term? I haven’t seen anything yet that addresses this. The DRC+ data all seem to involve comparisons of one year with another, or with a small number of years, if I understand them correctly. Descriptiveness, I think, is defined as correlation of DRC+ in one year with that in the preceding year, or possibly several years.

                              But note these points:

                              Descriptiveness usually generates the highest correlations among the Measures, which makes sense given that all metrics are based on past results. But since we know that the raw descriptive measurements are “wrong,” we see descriptive performance as little more than an overall reality check. In fact, the higher the Descriptiveness, the more suspicious we are, because the best descriptive fits arise from overfitting past outcomes—precisely the problem we are trying to avoid.
                              The question I would ask Judge is: if you determine the DRC+ values for each year of a player’s multi-year career, then average them, would that provide a better description of his real production than if you averaged his wRC+ for each year over the same period? Judge is claiming that for each and every year, DRC+ is a better description of the hitter’s productivity than wRC+, but is it still possible that wRC+ averages out to be a superior description? I would think not, but I haven’t seen this addressed.

                              The use of Predictiveness as a measure may seem to contradict what was said above about avoiding projections, but it does not: like other measures, future performance has value in checking the validity of past results. But it is not our primary goal, because we are trying to derive the player’s expected past contributions, not forecast the future.
                              https://www.baseballprospectus.com/n...-contribution/


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