here,<\/a> who proposed it is rational to take Zips 3-year forward projections and append them to young players short history to develop a more comprehensive view of said player\/prospect. \u00a0So, I joined 5,700 Zips projections that came out a few weeks ago with the 19,000 prospect ratings, covering some 4,300 different prospects.\u00a0 No easy feat. Been working on this all winter, and not done yet.<\/p>\nAdvertisement<\/p>\n
In essence, as I describe players\/prospects, I am describing what Zip+DC thinks this player is today and will be 3 years hence.\u00a0 If you accept projections as a reasonable source of analysis, then I\u2019ve shortened my window to 4-7 years, which gets me inside that 2017 cut-off (I can find no reliable electronic data source of prospect grades prior to that year).<\/p>\n
I had to make a couple other methodological choices which I invite you to comment on.\u00a0 One is, I\u2019ve calculated each player\/prospects\u2019 actual+projected WAR value and divided that value by that player\u2019s MLB seasons \u2013 1 ti create an \u201caverage WAR\u201d.\u00a0 Zips appears to forecast everyone 3 years out, including young-ish prospects such as Raniel Rodriguez, which I found handy.\u00a0 Thus, every player has a minimum of 3 seasons of data, more if they\u2019ve made their MLB debut.\u00a0\u00a0 I used that AverageWAR to assign an FV value of what that player is today and expected to be in the future, as compared to his prospect peers (not all players). \u00a0\u00a0This value is completely driven by Zips projections plus actual production and stands in contrast to the scouting grades I compared them with.<\/p>\n
Then, I distributed that players Average WAR along the 20-80 scale, using the guidance that each 10 places is one standard deviation.\u00a0 Ergo, 68% of all prospects will have what I term \u201cAdjusted FV\u201d between 40 and 60 and 98% will fall between FV 30 and FV 70 and the remaining 5% will occupy the nether regions 20 and 80 FV.\u00a0 In practice, I ended up with more 20\u2019s and 30\u2019s because many prospects don\u2019t make an MLB debut, don\u2019t achieve and 35 or higher FV and have no actual production nor any Zips ratings, so they go into the waste bin.<\/p>\n
My first test was to evaluate the prospect\/players who grade out 80 from their performance and projection.\u00a0 A total of 5.\u00a0 The rarest of the rare, top .3%. You can see the list below.\u00a0 Definitely performances that are outlier (beyond 2 standard deviations from average).\u00a0 The list passes the eye test, no?<\/p>\n
PlayerName<\/p>\n
primary_position_name<\/p>\n
pitcher.type<\/p>\n
careerWAR<\/p>\n
projectedWAR<\/p>\n
firstFV<\/p>\n
lastFV<\/p>\n
Adjusted.FV<\/p>\n
Shohei Ohtani<\/p>\n
Two-Way Player<\/p>\n
Starter<\/p>\n
49.6779<\/p>\n
18.82581<\/p>\n
70<\/p>\n
70<\/p>\n
80<\/p>\n
Shohei Ohtani<\/p>\n
Two-Way Player<\/p>\n
Starter<\/p>\n
49.6779<\/p>\n
18.82581<\/p>\n
70<\/p>\n
70<\/p>\n
80<\/p>\n
Tarik Skubal<\/p>\n
Pitcher<\/p>\n
Starter<\/p>\n
19.2694<\/p>\n
17.80266<\/p>\n
45<\/p>\n
60<\/p>\n
80<\/p>\n
Bobby Witt Jr.<\/p>\n
Shortstop<\/p>\n
Starter<\/p>\n
26.7328<\/p>\n
17.60179<\/p>\n
55<\/p>\n
65<\/p>\n
80<\/p>\n
Garrett Crochet<\/p>\n
Pitcher<\/p>\n
Reliever<\/p>\n
11.8924<\/p>\n
15.56059<\/p>\n
45<\/p>\n
50<\/p>\n
80<\/p>\n
Paul Skenes<\/p>\n
Pitcher<\/p>\n
Starter<\/p>\n
10.77<\/p>\n
15.51154<\/p>\n
60<\/p>\n
65<\/p>\n
80<\/p>\n
I scaled the AdjustedFV value by starters, relievers, and position players.\u00a0 In the list above, you are seeing the top .3% of each group.\u00a0 No relievers performed at 80 FV, FG tends to scale all pitchers to WAR per 200 IP for comparison purposes, but I found the 200 IP limit a bit anachronistic (and this is a modern data set and who pitches 200 IP anymore?) and leverage varies a lot between starters and reliever, so I chose to scale within a like cohort.\u00a0 Tell me if you agree with a list that shows Devin Williams as more valuable since he broke in than say, Dakota Hudson.<\/p>\n
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If this passes the eye test, then the whole data produces some MLB-wide averages we can begin to compare.<\/p>\n
GroupTotal<\/p>\n
MultipleRankings<\/p>\n
TrendDown<\/p>\n
TrendDownPct<\/p>\n
TrendUp<\/p>\n
TrendUpPct<\/p>\n
Traded<\/p>\n
TradedPct<\/p>\n
NoChgPct<\/p>\n
BeatProjection<\/p>\n
BeatProjectionPct<\/p>\n
UndershotProjection<\/p>\n
UndershotPct<\/p>\n
HitProjection<\/p>\n
HitProjectionPct<\/p>\n
4297<\/p>\n
3710<\/p>\n
920<\/p>\n
25%<\/p>\n
910<\/p>\n
25%<\/p>\n
713<\/p>\n
19%<\/p>\n
51%<\/p>\n
1159<\/p>\n
27%<\/p>\n
2846<\/p>\n
66%<\/p>\n
292<\/p>\n
7%<\/p>\n
Here we see the total of 4,297 players.\u00a0 Most (3,710) of the players have more than one ranking, so for most prospects we can see how they evolved a bit in the minor leagues. Note that as prospects are re-evaluated (annually or semi-annually), roughly 25% go up and roughly 25% go down.\u00a0 A nice even distribution.\u00a0 I find myself surprised that 50% of original rankings remain unchanged through a minor leaguer\u2019s career.\u00a0 Realize that 3,700 players get 18,500 rankings, so that tells me initial FV grades remain pretty static across MiLB.\u00a0 Interesting.\u00a0 I expected more volatility.<\/p>\n
Another tidbit to observe.\u00a0 About 15% of prospects get traded during their MiLB career.\u00a0\u00a0 This number is undoubtedly a bit higher, but in the data I only see prospects who 1) change teams, 2) have a high enough ranking that they are ranked in both organizations.\u00a0 Is 15% a surprisingly high or low number to you?<\/p>\n
Here is the fun one.\u00a0 In spite of the somewhat sticky nature of initial FV grades given, actual output + current projection, when converted to the AdjustedFV, results in a 6% hit rate.\u00a0 Said another way, 94% of prospects who go on to accumulate enough juice to collect actual fWAR or gain a 3-year projection come in at least \u00bd of a standard deviation off their initial FV grade (which half the time is their final FV grade, too).<\/p>\n
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That is a league wide look across all prospects.\u00a0 To get closer to how the Cardinals are doing, I wanted to break it down by each FV grade.<\/p>\n
firstFV<\/p>\n
GroupTotal<\/p>\n
MultipleRankings<\/p>\n
TrendDown<\/p>\n
TrendDownPct<\/p>\n
TrendUp<\/p>\n
TrendUpPct<\/p>\n
Traded<\/p>\n
TradedPct<\/p>\n
NoChgPct<\/p>\n
BeatProjection<\/p>\n
BeatProjectionPct<\/p>\n
UndershotProjection<\/p>\n
UndershotPct<\/p>\n
HitProjection<\/p>\n
HitProjectionPct<\/p>\n
35<\/p>\n
2<\/p>\n
2<\/p>\n
0<\/p>\n
0%<\/p>\n
2<\/p>\n
100%<\/p>\n
0<\/p>\n
0%<\/p>\n
0%<\/p>\n
1<\/p>\n
50%<\/p>\n
1<\/p>\n
50%<\/p>\n
0<\/p>\n
0%<\/p>\n
37.5<\/p>\n
1369<\/p>\n
1194<\/p>\n
2<\/p>\n
0%<\/p>\n
360<\/p>\n
30%<\/p>\n
195<\/p>\n
16%<\/p>\n
70%<\/p>\n
338<\/p>\n
25%<\/p>\n
1031<\/p>\n
75%<\/p>\n
0<\/p>\n
0%<\/p>\n
40<\/p>\n
1722<\/p>\n
1447<\/p>\n
398<\/p>\n
28%<\/p>\n
282<\/p>\n
19%<\/p>\n
302<\/p>\n
21%<\/p>\n
53%<\/p>\n
441<\/p>\n
26%<\/p>\n
1029<\/p>\n
60%<\/p>\n
252<\/p>\n
15%<\/p>\n
42.5<\/p>\n
421<\/p>\n
388<\/p>\n
172<\/p>\n
44%<\/p>\n
91<\/p>\n
23%<\/p>\n
73<\/p>\n
19%<\/p>\n
32%<\/p>\n
97<\/p>\n
23%<\/p>\n
317<\/p>\n
75%<\/p>\n
7<\/p>\n
2%<\/p>\n
45<\/p>\n
472<\/p>\n
409<\/p>\n
213<\/p>\n
52%<\/p>\n
107<\/p>\n
26%<\/p>\n
89<\/p>\n
22%<\/p>\n
22%<\/p>\n
175<\/p>\n
37%<\/p>\n
287<\/p>\n
61%<\/p>\n
10<\/p>\n
2%<\/p>\n
47.5<\/p>\n
77<\/p>\n
70<\/p>\n
25<\/p>\n
36%<\/p>\n
27<\/p>\n
39%<\/p>\n
6<\/p>\n
9%<\/p>\n
26%<\/p>\n
22<\/p>\n
29%<\/p>\n
52<\/p>\n
68%<\/p>\n
3<\/p>\n
4%<\/p>\n
50<\/p>\n
140<\/p>\n
125<\/p>\n
63<\/p>\n
50%<\/p>\n
27<\/p>\n
22%<\/p>\n
32<\/p>\n
26%<\/p>\n
28%<\/p>\n
60<\/p>\n
43%<\/p>\n
75<\/p>\n
54%<\/p>\n
5<\/p>\n
4%<\/p>\n
55<\/p>\n
65<\/p>\n
55<\/p>\n
38<\/p>\n
69%<\/p>\n
10<\/p>\n
18%<\/p>\n
13<\/p>\n
24%<\/p>\n
13%<\/p>\n
22<\/p>\n
34%<\/p>\n
40<\/p>\n
62%<\/p>\n
3<\/p>\n
5%<\/p>\n
60<\/p>\n
22<\/p>\n
19<\/p>\n
8<\/p>\n
42%<\/p>\n
4<\/p>\n
21%<\/p>\n
3<\/p>\n
16%<\/p>\n
37%<\/p>\n
1<\/p>\n
5%<\/p>\n
11<\/p>\n
50%<\/p>\n
10<\/p>\n
45%<\/p>\n
65<\/p>\n
3<\/p>\n
1<\/p>\n
1<\/p>\n
100%<\/p>\n
0<\/p>\n
0%<\/p>\n
0<\/p>\n
0%<\/p>\n
0%<\/p>\n
0<\/p>\n
0%<\/p>\n
3<\/p>\n
100%<\/p>\n
0<\/p>\n
0%<\/p>\n
70<\/p>\n
4<\/p>\n
0<\/p>\n
0<\/p>\n
NA<\/p>\n
0<\/p>\n
NA<\/p>\n
0<\/p>\n
NA<\/p>\n
NA<\/p>\n
2<\/p>\n
50%<\/p>\n
0<\/p>\n
0%<\/p>\n
2<\/p>\n
50%<\/p>\n
Here, you see the same interesting tidbits, broken down by FV. \u00a0We can safely ignore the extreme ends of the spectrum (20,30, 70, 80) as small sample size, but in the middle seems to be a story.<\/p>\n
For instance, we can see in this view that most players traded (~500 of the total ~700) fall in the FV 40 or FV 40+ ranks.\u00a0 Very few teams trade 45+ and up players, partially because they don\u2019t have many to trade.\u00a0 When evaluating trades for prospects, take note.\u00a0 Remember this when we get to the Cardinals.<\/p>\n
Also note that FV groups that tend to trend up the most are 35+, 45 and 45+.\u00a0\u00a0 Almost universally, the trend downs tend to cluster in the upper-echelons of initial rankings.\u00a0 When evaluating draft picks, take note.\u00a0 FV 45 is an odd group.\u00a0 By far and away the group of players most likely to move off an initial 45 rating, going down 50% of the time.<\/p>\n
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Take a look at the \u201cundershoot\u201d column.\u00a0 These are the prospects who have performed (or are projected to perform) lower than their initial FV at draft\/signing time.\u00a0 A rule of thumb would something like 70% of prospects undershoot.\u00a0 The percentages improve a bit with the FV 55 and up group, but those numbers are so small that a large SSS stamp is posted on them.<\/p>\n
So, this is a Cardinals blog after all, so we should talk about them, no?<\/p>\n
Here is \u00a0the Cardinal prospect-only breakdown, following the same pattern.<\/p>\n
firstFV<\/p>\n
GroupTotal<\/p>\n
MultipleRankings<\/p>\n
TrendDown<\/p>\n
TrendDownPct<\/p>\n
TrendUp<\/p>\n
TrendUpPct<\/p>\n
Traded<\/p>\n
TradedPct<\/p>\n
NoChgPct<\/p>\n
BeatProjection<\/p>\n
BeatProjectionPct<\/p>\n
UndershotProjection<\/p>\n
UndershotPct<\/p>\n
HitProjection<\/p>\n
HitProjectionPct<\/p>\n
37.5<\/p>\n
47<\/p>\n
41<\/p>\n
0<\/p>\n
0%<\/p>\n
14<\/p>\n
34%<\/p>\n
10<\/p>\n
24%<\/p>\n
66%<\/p>\n
11<\/p>\n
23%<\/p>\n
36<\/p>\n
77%<\/p>\n
0<\/p>\n
0%<\/p>\n
40<\/p>\n
55<\/p>\n
52<\/p>\n
9<\/p>\n
17%<\/p>\n
12<\/p>\n
23%<\/p>\n
21<\/p>\n
40%<\/p>\n
60%<\/p>\n
17<\/p>\n
31%<\/p>\n
32<\/p>\n
58%<\/p>\n
6<\/p>\n
11%<\/p>\n
42.5<\/p>\n
13<\/p>\n
12<\/p>\n
3<\/p>\n
25%<\/p>\n
5<\/p>\n
42%<\/p>\n
3<\/p>\n
25%<\/p>\n
33%<\/p>\n
4<\/p>\n
31%<\/p>\n
9<\/p>\n
69%<\/p>\n
0<\/p>\n
0%<\/p>\n
45<\/p>\n
14<\/p>\n
13<\/p>\n
8<\/p>\n
62%<\/p>\n
1<\/p>\n
8%<\/p>\n
6<\/p>\n
46%<\/p>\n
31%<\/p>\n
7<\/p>\n
50%<\/p>\n
5<\/p>\n
36%<\/p>\n
2<\/p>\n
14%<\/p>\n
47.5<\/p>\n
1<\/p>\n
1<\/p>\n
0<\/p>\n
0%<\/p>\n
0<\/p>\n
0%<\/p>\n
0<\/p>\n
0%<\/p>\n
100%<\/p>\n
0<\/p>\n
0%<\/p>\n
1<\/p>\n
100%<\/p>\n
0<\/p>\n
0%<\/p>\n
50<\/p>\n
12<\/p>\n
11<\/p>\n
3<\/p>\n
27%<\/p>\n
2<\/p>\n
18%<\/p>\n
6<\/p>\n
55%<\/p>\n
55%<\/p>\n
7<\/p>\n
58%<\/p>\n
5<\/p>\n
42%<\/p>\n
0<\/p>\n
0%<\/p>\n
55<\/p>\n
2<\/p>\n
1<\/p>\n
1<\/p>\n
100%<\/p>\n
0<\/p>\n
0%<\/p>\n
1<\/p>\n
100%<\/p>\n
0%<\/p>\n
0<\/p>\n
0%<\/p>\n
2<\/p>\n
100%<\/p>\n
0<\/p>\n
0%<\/p>\n
See anything?\u00a0 First, the high floor, low ceiling draft approach jumps out.\u00a0 Interesting, even now, they are, percentage wise, a little light in the 40+, 45 and 45+ ranges.\u00a0 That is after all the trades and last year\u2019s draft.<\/p>\n
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One thing that stands out to me. The Cardinals have been involved in trades of their 45 and 50 FV prospects at double the rate of league average.\u00a0 Remember when I wrote earlier that teams don\u2019t appear to like to trade these prospects?\u00a0 The Cardinals were involved in 6 of the 32 FV 50 trades over the period.\u00a0 I gather that is mostly a reflection of this past off-season, but I have not proven that yet. I would hate to see how this data looked for the Cardinals before, say June, 2025.<\/p>\n
Although the numbers are small, it is noteworthy that 11 of 15 players 45+ or higher have (or are) under their original draft projection. \u00a0\u00a0That would be about 70%, or right on league average for undershoot in that range.\u00a0 My takeaway on this?\u00a0 Probably that the Cardinal\u2019s development program, in falling back, fell back to league-average.\u00a0 Not good enough to sustain their competitive model but not collapsed either.<\/p>\n
I could go a lot of ways with this data.\u00a0 More clean-up is needed.\u00a0 Would love to backcast a bit farther.\u00a0 Thoughts? \u00a0Questions?\u00a0 What made you wonder about that I can explore more?<\/p>\n
I\u2019m off to Florida early next week.\u00a0 I will report back while I\u2019m there and recap after I return.\u00a0 If you have any questions for me to explore, put it in comments and I will try.\u00a0 I have an extensive list.\u00a0 I believe my press credentials are ready.<\/p>\n","protected":false},"excerpt":{"rendered":"Spring Training is underway!\u00a0 There is new stuff coming out of camps everyday now, a respite from the…\n","protected":false},"author":2,"featured_media":583100,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2403],"tags":[5,1351,160,9145,83,46954,922,4,21995,877,709,468,673,67,4311,4310,382,4391],"class_list":{"0":"post-583099","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-st-louis-cardinals","8":"tag-baseball","9":"tag-bobby-witt-jr","10":"tag-cardinals","11":"tag-dakota-hudson","12":"tag-devin-williams","13":"tag-fv","14":"tag-garrett-crochet","15":"tag-mlb","16":"tag-mlb-players","17":"tag-paul-skenes","18":"tag-prospects","19":"tag-shohei-ohtani","20":"tag-st-louis","21":"tag-st-louis-cardinals","22":"tag-stlouis","23":"tag-stlouiscardinals","24":"tag-tarik-skubal","25":"tag-the-cardinals"},"share_on_mastodon":{"url":"https:\/\/channels.im\/@mlb\/116103787566579593","error":""},"_links":{"self":[{"href":"https:\/\/www.rawchili.com\/mlb\/wp-json\/wp\/v2\/posts\/583099","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.rawchili.com\/mlb\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.rawchili.com\/mlb\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.rawchili.com\/mlb\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.rawchili.com\/mlb\/wp-json\/wp\/v2\/comments?post=583099"}],"version-history":[{"count":0,"href":"https:\/\/www.rawchili.com\/mlb\/wp-json\/wp\/v2\/posts\/583099\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.rawchili.com\/mlb\/wp-json\/wp\/v2\/media\/583100"}],"wp:attachment":[{"href":"https:\/\/www.rawchili.com\/mlb\/wp-json\/wp\/v2\/media?parent=583099"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.rawchili.com\/mlb\/wp-json\/wp\/v2\/categories?post=583099"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.rawchili.com\/mlb\/wp-json\/wp\/v2\/tags?post=583099"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}