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Christian Yelich and the sophomore slump, Part 1

Christian Yelich is off to a slow start. Is it a natural regression from a breakout season or is it sobering indication of his true value ? This week we seek to answer these questions by examining various sophomore seasons of breakout players to identify whether or not Yelich's slow start is cause for concern.

Early season struggles
Early season struggles
Jason Getz-USA TODAY Sports

The Marlins are currently in a bad way. There's no sugar-coating it. This was not the start to the season the team envisioned for themselves after a particularly strong spring training. A combination of poor starting pitching, lackluster hitting and a parade of untimely injuries have left the Marlins in the cellar of a decidedly weak division. This, coupled with the recent notion that ownership is losing its patience with current manager Mike Redmond, has left the Marlin's 2015 campaign on thin ice.

One of the most troubling concerns for early season prognosticators has been the lack of offense from key contributors; Christian Yelich among them. Coming off of a breakout campaign in 2014 and the signing of an offseason contract worth $49.57 Million over seven years, Christian Yelich has not begun the young season how many had hoped.

Name Season Team G PA HR R RBI SB BB% K% ISO BABIP AVG OBP SLG wOBA wRC+ BsR Off Def WAR
Christian Yelich 2014 Marlins 144 660 9 94 54 21 10.60% 20.80% .119 .356 .284 .362 .402 .341 116 3.8 15.8 4 4.3
Christian Yelich 2015 Marlins 11 49 0 4 4 1 8.20% 26.50% .022 .281 .200 .265 .222 .226 42 0.2 -3 -0.5 -0.2

His overall production is down, his power has failed to appear thus far, his strikeout rate has increased and his walk rate has decreased. In general he is performing much worse than expected. What could be the cause? It's important to note that he has been dealing with a nagging injury. Also, the season is just starting and as such the obligatory reminders of sample sizes are always in play. Then again, with Yelich starting his second season there is the notion of the "Sophomore Slump". A cruel demonstration of regression, the "slump" has been known to occur to even the greatest of players. Ordinarily one would shrug off these worries as a natural part of the maturation process of a young player. Unfortunately for Yelich he is not afforded this luxury. After all, Yelich did just ink a hefty contract especially for a player who has only accrued one full season of service time. Between his newly minted contract,  his stellar rookie season and the high expectations that have been saddled on him, any flicker of failure is sure to send the fan base into a frenzy (if it hasn't already).

In order to determine whether or not the "Sophomore Slump" exists I have identified and selected eight candidates to examine their platform year and their subsequent contract year. The goal being to note any apparent regressions in performance or lack thereof.

The following list of names originated on Baseball Prospectus'  Cot's Baseball Contracts and were further determined by the following prerequisites:

1) They must have accrued no more than one (1) full season before signing a long term contract.

2) They must be position players not pitchers.

3) The Platform Year represents the season immediately prior to the new contract taking effect.

4) The Contract Year represents the first season of the new contract.

5) Overall regression of performance is based off of the difference in WAR between the PY and the CY

The list, not surprisingly, is short:

Name Season Team G PA HR R RBI SB BB% K% ISO BABIP AVG OBP SLG wOBA wRC+ BsR Off Def WAR
Andrelton Simmons 2013 Braves 157 658 17 76 59 6 6.10% 8.40% .149 .247 .248 .296 .396 .303 91 -2.6 -9.4 31.6 4.5
Andrelton Simmons 2014 Braves 146 576 7 44 46 4 5.60% 10.40% .087 .263 .244 .286 .331 .273 71 -3.1 -21.6 22.1 1.9
Jedd Gyorko 2013 Padres 125 525 23 62 63 1 6.30% 23.40% .195 .287 .249 .301 .444 .325 111 0.3 6.7 0.5 2.5
Jedd Gyorko 2014 Padres 111 443 10 37 51 3 8.10% 22.60% .123 .253 .210 .280 .333 .275 78 -1.6 -12.8 -0.2 0
Ryan Braun 2007 Brewers 113 492 34 91 97 15 5.90% 22.80% .310 .361 .324 .370 .634 .421 155 1.3 36.3 -26.9 2.5
Ryan Braun 2008 Brewers 151 663 37 92 106 14 6.30% 19.50% .268 .305 .285 .335 .553 .376 129 4.2 28.2 -3.9 4.6
Troy Tulowitzki 2007 Rockies 155 682 24 104 99 7 8.40% 19.10% .189 .335 .291 .359 .479 .364 109 0.4 8.4 22.2 5.2
Troy Tulowitzki 2008 Rockies 101 421 8 48 46 1 9.00% 13.30% .138 .289 .263 .332 .401 .321 83 -4.1 -13.3 4.4 0.5
Nomar Garciaparra 1997 Red Sox 153 734 30 122 98 22 4.80% 12.50% .228 .315 .306 .342 .534 .374 122 0.6 20.9 18.3 6.4
Nomar Garciaparra 1998 Red Sox 143 652 35 111 122 12 5.10% 9.50% .262 .311 .323 .362 .584 .402 139 -0.2 32.6 18.7 7.3
Brian McCann 2006 Braves 130 492 24 61 93 2 8.30% 11.00% .240 .332 .333 .388 .572 .401 142 -7 19.9 7.7 4.3
Brian McCann 2007 Braves 139 552 18 51 92 0 6.30% 13.40% .183 .282 .270 .320 .452 .329 95 -6.9 -10.4 7.8 1.5
Carlos Santana 2011 Indians 155 658 27 84 79 5 14.70% 20.20% .217 .263 .239 .351 .457 .351 124 -2.4 15.9 -5.9 3.3
Carlos Santana 2012 Indians 143 609 18 72 76 3 14.90% 16.60% .168 .278 .252 .365 .420 .344 120 -8.1 6.1 1.8 2.9
Grady Sizemore 2005 Indians 158 706 22 111 81 22 7.40% 18.70% .195 .334 .289 .348 .484 .359 123 2.8 22.3 9.5 5.6
Grady Sizemore 2006 Indians 162 751 28 134 76 22 10.40% 20.40% .243 .339 .290 .375 .533 .383 132 9.2 40.5 15.2 7.9
Christian Yelich 2014 Marlins 144 660 9 94 54 21 10.60% 20.80% .119 .356 .284 .362 .402 .341 116 3.8 15.8 4 4.3
Christian Yelich* 2015 Steamer (U) 140 638 12 75 56 17 9.60% 21.20% .125 .324 .262 .335 .387 .322 103 1.4 3.8 -2.1 2.3

The results were mixed. Three players played better in their second season while five played worse. Those that showed regression tended to have significant decreases in ISO and BABIP which subsequently affected their slash lines and overall production in counting statistics. Additionally, those that showed the most regression demonstrated severe decreases in walk rate while increase their strikeout dramatically. In short, regression reared its ugly head in the sophomore season by a combination of a lack of power, a lack of luck, and poor plate discipline. WAR values dipped even further if there was evidence of declining defensive value.

These negative trends provide stark contrast to the three players that improved in their sophomore seasons. Generally speaking, these players performed similarly in both ISO and BABIP while improving walks and strikeouts. These improvements coupled with added defensive values helped bolster their WAR and in many cases caused an added value.

While this evidence demonstrates that natural regression is likely to occur in a player;'s second season, many questions still remain. Questions such as: What allowed for Braun, Garciaparra and Sizemore to repeat their performance? What does this pattern of regression mean for stars quickly signed to contracts after breakout seasons? Does performance rebound after disappointing sophomore seasons? Are players signed to contracts after their second, third and fourth seasons likely to slump after signing a big contract? All these questions and more will be explored in Part II of this series.