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College Soccer News - 2017 Top Fifteen Recruiting Classes

March 3, 2017 - College Soccer News issues its initial rankings of the Top 15 Men's College Soccer Recruiting Classes in 2017 based on the information available at this time. The rich tend to get richer on the recruiting trail as the elite programs with a winning tradition always attract top talent. However there always are a few surprises as was the case again in 2017.

It should be noted that the landscape in terms of top classes may change somewhat in the coming months as additional information becomes available regarding who is going to go where. As a result College Soccer News will update its rankings in August based on the additional information that may be available at that time and will expand its rankings to include the top twenty-five classes in the country.  

#1

Stanford

Jeremy Gunn brings in the best class in the country, and continues to reload the two time defending national champions.  Even with the loss of Alex Rose to the University of North Carolina this class is as loaded as any class in the country, especially on the offensive side of the ball.

The number one class includes Carson Vom Steeg, Logan Panchot, Rhys De Sota, Charlie Wehan, Andrew Apahamian, Kei Tomozawa, Arda Bulut, Jack O,Brien, and Zachary Ryan. 

#2

North Carolina

Head Coach Carlos Somoano and UNC continue to reload to what is already one of the strongest teams in the country.  Now add Johnny Nelson the best defender in the class, and Alex Rose who could end up being the best offensive player in the class and the rich just got richer.  If all these players stay on track and end up on Carolina’s campus in the fall this could be a team that makes a run into December.

The number two class includes John Nelson, Alex Rose, Lucas Del Rosario, Raul Aguilera, Mark Salas, Alec Smir, and Charlie Forecast. . 

#3

Wake Forest

Here is some news - Dane Brenner and Bobby Muuss can recruit. It isn’t often that you see a coach play his former team in the final four just after leaving his former program but that was the case when Wake Forest played Denver last year, and the country got to see first hand the type of talent Bobby Muuss brings where ever he goes.  Wake Forest loses some key pieces from last year but continues to reload with another stellar class.

The number three class includes Brandon Servania, Omir Fernandez, Mark McKenzie, Machop Chol, Andrew Pannenberg, Landen Haig, Kyle McCurley, and Dominic Peters. 

#4

Indiana

Recruiting Coordinator Kevin Robson made waves with this recruiting class for his head coach Todd Yeagley.  The offensive fire power coming into Bloomington might be enough in the coming years to push IU back to national contention, if all the players stay.  Rumor is Justin Renicks has European offers which could hurt IU if he forgoes college.  Even if Rennicks leaves Dorsey, Warr and Toye are enough to keep IU as a top 10 recruiting class and keep Hosier nation believing in what Coach Yeagley is building. 

The number four class consists of Griff Dorsey, Justin Rennicks, Thomas Warr, Mason Toye, Joacob Meier, Joseph Schmidt, Kyle Barks and Jacob Gruber. 

#5

Portland

It’s tough for anyone to argue that Nick Carlin-Voigt isn’t one of the best recruiters in the nation.  From the talent level that left UCLA after this season, to what he has been able to bring in now as head coach at Portland, Voigt continues to attract the best of the best wherever he goes. Portland’s program which made the NCAA tournament from out of nowhere was one of the surprises of college soccer last season, and this class should help keep them as an NCAA tournament team.

The number five class consists of transfers Jason Romero and Jabari Newton and freshman Caua Soares, Jason Manning, Luca Defreitas-Hansen, Jake Kemhadjian, Kevein Capelao, Easton Embley, Iray Hamuli, Lucas Van Eynde. 

#6

Syracuse

Ian McIntyre continues to bring Syracuse to new levels every year he is at the helm.  His recruiting classes over the last five years have proven to be winners all over the park.  This year’s class is no different.  Expect Sondre Norheim to be the name out of this class that sticks. 

The number six class consists of Simon Triantafillou, Miles Franklyn, Tajon Buchanan, Lukas Rubio, Justin Earle, Dylan McDonald, Evans Peters, Matthew Pickard, Nikolas Steiner, and Sondre Norheim. 

#7

Ohio State

Ohio State struggled with injuries last year that crippled them in terms of results.  Recruiting coordinator Ian Gordona was tasked to rebuild a program that lost 8 seniors and one player to the MLS early under head coach John Bluem, and from the looks of it that’s exactly what he did. Ohio State added depth in every position and might have the deepest core of goalies in the whole country.  With one of the largest classes in the country OSU seemed to add depth everywhere needed to give them a better outlook for 2017.

The number seven class consists of Devyn Etling, David Abonce, Xavier Kennedy, Jared Anderson, Mitchell Bergman, Chase Carraher, Jacob Goyen, Gabe Harms, Will Hirschman, Joshua Jackson Ketchup, and Matt Kiley. 

#8

Georgetown

Georgetown had an off year by way of a normal Brian Wiese side, but considering all Georgetown lost to the MLS in 2016 it was to be expected.  Georgetown’s 2017 class is loaded with players that will add depth and talent everywhere going forward.

The number eight class consists of Jacob Montes, Sean O'Hearn, Foster McCune, Jack Beer, Derek Dodson, Witt Conger, Rio Hope Gund, Ethan Koelher and Chris Le. 

#9

Michigan

Few people can fault recruiting coordinator Tommy McMenemy for the talent that Michigan has brought in to Ann Arbor over the last five years under head coach Chaka Daley.  Talent-wise Michigan has enough to be as good as any team in the country, but the results haven’t been there.  Michigan’s 2017 class is athletic and gifted and could be enough to get them back into the NCAA tournament.    

The number nine class includes Mohammed Zakyi, Umar Farouk Osman, Marc Ybarra, Jacob Nunner, Nash Pirie, Jackson Pirie, Jackson Ragen, Austin Swiech, and Carlos Tellez. 

#10

Virginia

Some things never change, and for the University of Virginia, the one constant is top ten recruiting classes.  Associate Head Coach Terry Boss brings in a wealth of talent for a program that didn’t lose much from last season’s NCAA tournament run under head coach George Gelnovatch. 

The number ten class includes two big transfers to note in Cameron Harr from Marist and Prosper Figbe from USF to go along with freshman Justin Ingram, Connor Jones, Faris Abdi, Brad Kurtz, Spencer Patton and Ahdan Tait. 

#11

Duke 

Like every year under Coach Kerr Duke will boast one of the most talented teams in the country, the question will be can the talent translate into results. Adding US National team goalie Will Pulisic from Borussia Dormund will certainly help. Kerr, lands the type of class that will add depth and talent all over the field. 

The number eleven class includes Daniel Wright, Wilhelm Jacques, Will Pulisic, Aidan Foster, Stephen O'Connell and Michael Reis. 

#12

Maryland

Recruiting Coordinator Brian Rowland has proven to be one of the better recruiters in the country, and the MLS producing factory that is the University of Maryland under coach Sasho Cirovski just continues to roll.  Expect there to be one of two internationals still left out of the recruiting class. 

The number twelve class includes Eric Matzelevich, Giovanni Vasquez, Matthew Di Rosa, Ben De Rosa, Paul Frendach, and Petr Janda. 

#13

North Carolina State

George Keifer’s new home comes with the notion of rebuilding. Keifer earned his stripes making South Florida a constant in the NCAA tournament. To help aid him in the rebuild his hire of Jeff Negalha to help with the recruiting is a very good start. N.C. State might still be two or three years away from being a tournament team, but this recruiting class will certainly help towards the future. 

The number thirteen class includes Emanuel Perez, David Loera, McChlery Gourlay, David Norris, Caleb Martinex, and Jose Luis Morales. 

#14

UCLA

Head Coach Jorge Salcedo saw the majority of his starting line-up go pro this off-season, many as underclassmen which is a reflection of the double-edge sword of having that type of talent. This recruiting class though small should improve as more players come across the board. 

The number fourteen class includes transfer Alex Knox from Wake Forest and freshmen Brandon Terwege, Andrew Terwege, Andrew Paoli, Milan Iloski, and Eric Iloski. 

#15

UC Santa Barbara

Another program that will have to reload this off season is UCSB who saw a handful of players exit the school early for the pro game and other programs.  A nine player recruiting class should help in the transition. 

The number fifteen class includes transfers Hanno Antoni and Ricardo Montoy and freshman Kaya Fabbretti, Omari Fontes, Pablo Adames, Kavian Kashani, Rodney Michael, Giovanny Acosta, Rhys Pak, and Nico Sacco. 

  

The Impact of Shots, Shots Against and Total Shots Ratio in College Soccer by Louis Joslyn, Nicholas Joslyn and Mark Joslyn

The Impact of Shots, Shots Against and Total Shots Ratio in College Soccer 

Without question the publication of Michael Lewis’s seminal book Moneyball, which examined the data analytic approach of the Oakland A’s baseball team, advanced interest in the use of statistical analysis in professional sports

While slower to recognize the value of analytics, many professional soccer clubs now embrace the “big data” movement (Anderson and Sally 2014).  Yet so far, below the pro level, where the vast majority of players and coaches perform, analytics is largely absent.  We hope to demonstrate the value of analytics by utilizing typical box score metrics available at all levels of college soccer.    

Shots

Because there are many more shots than goals, and good fortune is often the antecedent of goals (Anderson and Sally 2014), shots are a more stable and reliable measure to evaluate team performance (Grayson 2012a). To examine shots in the college game, we collected 2014 data from the NCAA web site that provides season box score team totals for the three men’s soccer divisions (NCAA 2014).  Data was gathered for 205 teams in Division 1, 204 in Division 2* and 406 in Division 3.

*Division 2 had three teams listed - Pureto Rico Mayaguez, Pureto Rico Rio Piedras, and Sioux Falls that did not provide data. 

 

Table 1.  Mean Team Shots and Standard Deviation for NCAA Men’s Soccer 2014 Season

Division          Shots per game          Std. dev

     1                          12.4                        2.1

     2                          13.4                        2.6

     3                          13.6                        3.3

Table 1 shows that average team shots per game were remarkably similar across divisions.  Division 1 teams took 12.4 shots, Division 2, 13.4 and Division 3, 13.6 shots per game.  The standard deviation, however, which is a measure of how widely teams are spread across the distribution of shots per match, is notably higher in Division 3. For example, St. Scholastica outpaced other Division 3 teams at 26 shots per match while Iowa Wesleyan was last at a curious .83.  Simon Fraser topped Division 2 at 23 shots per game and Dominican of California recorded the fewest shots at 7.1.  Wofford enjoyed the highest average in Division 1 at 18.2 while Howard produced a low of 6.4 shots per game. 

Shots and Shots Against

Manufacturing a high number of shots is perhaps desirable but shots should be considered in relation to the opponents’ offensive efforts.  Table 2 shows the relationship between shots per game and shots against.  We use correlations between shots and shots against to establish the direction and strength of association.  Correlations range from -1 to +1.  The closer the correlation to +1 or -1, the stronger the association between shots and shots against.  If the correlation is close to 0, there is little to no relationship between shots and shots against. A negative sign suggests that greater numbers of shots per game are associated with fewer shots against.  That is, the more a team shoots, the less shots for the opposition.The -.44 correlation for Division 1 suggest a moderately negative relationship.  However, in Division 2 and 3, the correlations are notably stronger yet still negative.

Table 2.  Correlations between Team Shots and Team Shots Against- 2014 Season  

Division          Correlations between Shots and Shots Against     

     1                                             - . 44   

     2                                             - . 63

     3                                             - . 62

By comparing shots to shots against, we draw two conclusions.  First, across three Divisions, high shot teams concede on average significantly fewer shots.  Second, the stronger correlations between shots and shots against in Division 2 and 3 suggest a greater number of extreme cases.  That is, significant disparities between average shots and shots against are more prevalent in Division 2 and 3 – thus greater shot imbalance among teams.   The substantially weaker correlation exhibited in Division 1 implies relatively greater shot balance, where shots and shots against are more equally distributed across teams.  

The Consequences of Shots and Shots Against

Shots naturally represent offensive chances; they bring to mind aggressive movement forward and frequent access to the final third.  Shots against, by contrast, denote defense and how well or poorly it performs.  An obvious question concerns the impact of shots and shots against. 

Do these measures influence the bottom line – wins? 

Our approach to this question is first to examine the teams that produced the most shots and those that conceded the fewest and compare their win percentages. 

The comparisons in Table 3 show that Creighton allowed the fewest shot attempts while producing the highest win percentage in Division 1 at 79.5%.  Wofford was best at generating shots but ranked 47th in win percentage at 63.9%. Similarly, in Division 2, Southern New Hampshire held opponents to fewest shots ranking 4th in win percentage at 90%.  Simon Fraser led in shots per game yet ranked 42nd in win percentage at 66.7%. Division 3 does not follow the pattern.  Methodist held teams to fewest shots but ranked 24th in win percentage while Saint Scholastica was first in shots but 4th in win percentage.

In general, Table 3 suggests that limiting the offensive chances of competitors produces more wins than generating the most shots.   

Table 3.  Most Shots, Fewest Conceded and Win Percentage – NCAA Men’s Soccer 2014             

            Division          Most Shots             Win %    Fewest Shots Against    Win %

            1                      Wofford                   63.9        Creighton                        79.5       

            2                      Simon Fraser           66.7        Southern NH                   90.0     

            3                      St. Scholastica        87.0         Methodist                       79.4            

We next used a more sophisticated statistical method called regression analysis to assess the effects of shots and shots against on win percentage.  This technique allows us to predict the win percentage of teams based on offensive and defensive shot measures, and determine which measure is the strongest predictor.  Regression analysis yields estimates that translate one additional shot per game, and one fewer shot against, into an increment of win percentage.  In this way we are able to determine how much each measure impacts win percentage while controlling for the impact of the other measure.

Results show that in Division 1, manufacturing one more shot per game was worth an additional 2.8 percent for a team’s season winning percentage.  Conceding one fewer shot per game increases winning percent by 3.3.  The difference, .5, represents the larger impact of the defensive measure – conceding fewer shots.  For example, if a team was able to reduce the number of shots against per game by two, win percentage for season would increase 6.6 percent – for a 20 game season this is equivalent to one more win.   

Additional analyses showed a nearly identical effect in Division 2.    One more shot per game in Division 2 increases winning percent for the season by 2.8 and one less shot conceded raises win percentage by 3.4 percent. 

For Division 3, shots and shots against contributed about equally to win percentage.  One more shot per game increases season win percent by 2.1, and one less against enhances winning by 2.4 percent.

Our analyses, then, points to the importance of tactical balance between offense and defense, but slightly favors defense:  It is better to concede one less shot than to produce one more.

Total Shots Ratio

It appears that successful teams shoot a lot and hold their opponents to fewer shots as well.  Analysts applied this logic in developing a measure called Total Shots Ratio (TSR), which is a difference ratio of shots and shots against (Goodman 2013; Grayson 2012b).  The measure is expressed as the ratio of shots a team takes versus the number of total shots. 

The equation, Total Shots For/ (Total Shots For + Total Shots Against) is a simple way of quantifying how frequently a team shoots compared to their opponents.  A ratio of .5 means the teams are matching shots, whereas a value over or under .5 indicates one team outshooting the other. 

Total Shot Ratio and Win Percentage

Figure 1 (below), which depicts the relationship between TSR and winning percentage in D3, shows that larger TSR values are in fact associated with higher winning percentages.  The correlation is very strong and positive at .83, nearly the same as in D2 .80 and a bit weaker in D1 .72.  The upper right quadrant of Figure 1 includes winning programs that also maintain high TSR values.  It is in this quadrant that the vast majority of NCAA D3 tournament teams are located – NCAA tourney teams colored in red.  The percentage of teams that qualified for the tournament possessing TSR values of .5 or higher, and winning 50 percent plus, are in fact striking across all Divisions – D3 92%, D2 95% and D1 75%.  The teams in the upper left quadrant of Figure 1 are likely successful counter attack teams, losing the shot contest but nevertheless winning the game. 

 

 

 

 

 

 

 

 

 

 

Conclusions

We draw several conclusions from the analyses. 

First, team shots per game were remarkably similar in the three college divisions.  In terms of shot production, a college soccer fan would experience comparable games across divisions.  However, the spread between the best and worst shot producing teams was largest in Division 3. 

Second, the data showed that shots and shots conceded were significant predictors of win percentage.  Shots conceded were a slightly better predictor, especially in Division 1 and 2.   Conceding 1 less shot per game increased win percentage to a greater extent than producing one more shot.  We now have estimates of the expected increase in win percentage given a specific coaching adjustment.  If a D1 coach, for example, adjusts defensive tactics for the coming season, and his/her team concedes three fewer shots per game as a result, while maintaining typical shot production totals, win percentage would be expected to increase by nearly 10 percent – equivalent to 2 games for a 20 game season.  Conceding three less shots a game is no easy task, but the payoff is considerable. 

Third, TSR, which is the share of total shots one team produces,is a powerful predictor of season win percentage.  TSR outperformed the singular measures that comprise it (shots and shots against), and among other box score metrics that precede goals – corners, saves, fouls - it is the most potent predictor of win percentage.The idea is straightforward:  Good teams shoot a lot, and they keep their opponents from shooting.  Given these results, it may be tempting to increase TSR by encouraging more shooting, or perhaps recruiting players that take every shot opportunity.  Shot volume matters, but TSR speaks to more than quantity.  Rather, we believe TSR is a reflection of a team’s control of play.  Shooting more than opponents generally implies control, and meaningful attacking possession.  TSR may then be an indicator of possession.  

We cannot test this assertion directly.   Unfortunately NCAA box scores do not include a possession statistic.  However, we can examine whether possession is related to TSR at the pro level.  For the 2014 MLS season we collected data from WhoScored.com and calculated the correlation between team possession percentage and TSR.   A significant positive correlation of .62 was discovered.  MLS teams that were excellent possession sides were also able to outshoot their opponents – exemplars were Sporting KC and LA Galaxy.  In the Spanish top flight La Liga, the correlation was even higher at .81.  Barcelona and Real Madrid maintained both possession and TSR levels higher than 60 percent.  Finally, in EPL’s 2013-14 season, the association between possession and TSR was .82. Manchester City ranked first in TSR and possession.  This evidence strongly supports our contention that TSR evokes control of play and indeed possession.  Though the correlation between TSR and possession is not a perfect one, especially in MLS, in the absence of a possession statistic college coaches may turn to TSR as a useful proxy. 

Finally, private firms such as Opta and Prozone are using technological advances to collect and maintain large inventories of advanced player and team data for professional clubs.  These data likely strengthen the role of statistical analyses for on and off the field decisions. 

By comparison, NCAA box scores are rudimentary and do not include such valuable metrics as shot location, possession, clearances, tackles, interceptions, to name a few.  While we analyzed team box score totals for the entire season, individual match data offers greater variation of key metrics.  However, collecting and coding well over 15,000 box scores represents a significant challenge.  It is precisely these types of barriers, - the resources required and expertise needed – that deter some programs from pursuing analytics (Hanlon 2015).   Here we offered several metrics that can be quickly calculated from box scores and used to evaluate divisions, teams, and tactics. 



About the authors:

Louis R. Joslyn played soccer at Simpson College where he was a Capital One CoSIDA Division 3 Men’s Soccer Academic All-American in 2014 and 2015. He is currentlypursing a PhD in Bioinformatics at the University of Michigan.

Nicholas J. Joslyn is currently a Junior Captain on Simpson College men’s soccer team and is pursing a Bachelor’s degree in Physics and Mathematics.  

Mark R. Joslynis a Professor in the Political Science Department of the University of Kansas and was the Baldwin Kansas High School Boys Soccer Coach from 2010 through 2014.


Mark R. Joslyn can be contacted at the Department of Political Science, 1541 Lilac Lane, 504 Blake Hall, University of Kansas, Lawrence, KS 66044 or via e-mail at This email address is being protected from spambots. You need JavaScript enabled to view it..  


References

Anderson, Chris and David Sally.  2014.  The Numbers Game:  Why Everything You Know about Soccer is Wrong.  Penguin Books. 

Goodman, Mike.  2013.  “What is Total Shots Ratio?  And How Can It Improve Your Understanding of Soccer?”  Grantland.com Accessed here: http://grantland.com/the-triangle/what-is-total-shots-ratio-and-how-can-it-improve-your-understanding-of-soccer/

Grayson James.  2012a.  “How Quickly Do Advanced Metrics Regress to their Final Values.”   James Blog.  Accessed at: https://jameswgrayson.wordpress.com/2012/11/27/how-quickly-do-the-advanced-stats-regress-to-their-final-values/

Grayson, James.  2012b.  “Another post about TSR”  James Blog.  Accessed at:  https://jameswgrayson.wordpress.com/2012/07/15/another-post-about-tsr/

Hanlon, Michael.  2014.  “Current Practices and Perceptions of Notational Analysis among United States Soccer Coaches.”  Soccer Journal.  Vol. 60, No. 1 p. 56-64.

Lewis, Michael.  2003.  MoneyBall:  The Art of Winning an Unfair Game.”  W.W. Norton & Co. New York.   

NCAA 2014.  http://www.ncaa.com/sports/soccer-men/d1


 

PODCAST: Chris Grassie (Marshall)

New Marshall head coach Chris Grassie joins the show this week to talk about moving from one of the top Division II programs in the country to take over the Thundering Herd program.

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This week's guest is new Marshall head coach Chris Grassie. Grassie helped make the University of Charleston a national Division II power winning six-straight conference titles and making two appearances in the NCAA championship game. Grassie talks about the transition between Division I and Division II, and why he thinks there isn't that much difference between the two. He also gives insight into his approach on scheduling and long-terms goals for the program.

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