TL;DR: I simulated 115,200 games against the twelve NSFL teams, upgrading the test team’s rookie 350ish TPE cornerback to a maxed out corner of each of the three CB archetypes. I tracked win percentage, points for and against, yards allowed, team interceptions, and individual interceptions and passes defended for the upgraded cornerback. I then ran significance tests on the numbers in an attempt to discover whether or not there is a superior cornerback archetype. Skip to the conclusions unless you like screenshots of google sheets.
Disclaimer: This study is not perfect. I’ll try to explain in as much detail what I did and why for those who are interested, and point out as many potential problems as I can. This is, however, the most comprehensive and actionable study done on this subject.
First, I’d like to thank @mithrandir and @iStegosauruz. mithrandir asked Steg a question on his podcast about studying CB archetypes. This was something I was already interested in studying, and had spoken about with people before. After listening to the podcast, I DM’d Steg and he very kindly gave feedback on my ideas and helped me to determine the methodology I used. mith’s question and subsequent study inspired me to finally do this, and Steg’s feedback helped to make this study far better than it would have been otherwise.
Methodology
I opened a rrecent sim file, the post S22 Week 4 file. I saved three additional versions of it. In the first, I upgraded my player, Brandon Booker, to max attributes for the All-Around archetype and set his height, weight and experience to 6’1, 210 lbs, 12 experience. In the second, I upgraded Booker to a max Man-to-Man corner at 6’3, 205 with 12 experience and in the third I upgraded Booker to a max Zone corner at 6’3, 205 with 12 experience. For tests 23 and 24 of each archetype (the games against Philadelphia) I upgraded Tyler Oles JR of the Arizona Outlaws as like Booker, he’s a rookie outside corner on a team just below .500 playing opposite a higher TPE corner. I don’t honestly know how important those similarities were, but doing that seemed sensible.
I then ran 1600 sims for each archetype, against each team, home and away. I chose 1600 because I can always sim 800 games at a time with no danger of a crash, but simming any more than that introduces a chance that the sim will crash on my laptop. 800 tests often throws up some weird results when I’m testing, so I wasn’t satisfied with 800 game samples. It took minimal additional effort for me to simulate 1600 games rather than, say, 1200 or 1500 so that’s what I did. I would’ve liked to do more so that I’d feel more confident in the results, but doing 1600 in each scenario gives me 115,200. Simulating that many games, exporting the .csv files and copy-and-pasting the relevant information already took long enough.
As Steg brought up during our conversations, a potential pitfall is that I may just be determining what the best build for Brandon Booker would be, or what the best build for an outside CB on the Liberty would be given the other players on the roster, or the best CB build for Week 4. These are valid concerns. This study would be better if I had obtained representative samples against every team using different tempos, playbooks and run:pass ratios and simmed those sets of games multiple times with different test teams and test players. Testing against all the different strats that teams have, might or will use would make this more future-proof, but would also take literally millions of sims. Using a random recent week was the compromise I settled for. Some teams used slower tempos, others faster, some used almost exclusively Spread, others were more balanced or power-heavy. Week 4 will not have perfectly captured the meta, but everything I tested against was a strat that NSFL teams have been using this season.
Finally, I ran significance tests on the results that I thought were the most important indicators using https://abtestguide.com/calc. I’m not a statistician, I took a stats module in university that I don’t remember much of. Please point it out if something looks obviously wrong.
Lots of Tables
![[Image: ebcd3889c3383ac2e2fad371de0c85cf.png]](https://gyazo.com/ebcd3889c3383ac2e2fad371de0c85cf.png)
Figure 1
![[Image: af1516fadd26add7db60f9d8c348b5c7.png]](https://gyazo.com/af1516fadd26add7db60f9d8c348b5c7.png)
Figure 2
![[Image: 9f11c3d23ed5bd9d2bb95214a171b475.png]](https://gyazo.com/9f11c3d23ed5bd9d2bb95214a171b475.png)
Figure 3
![[Image: bbe383e2b67d76a989cda3c98ffe0903.png]](https://gyazo.com/bbe383e2b67d76a989cda3c98ffe0903.png)
Figure 4
![[Image: 0f80909eaf1893261415430880f407c1.png]](https://gyazo.com/0f80909eaf1893261415430880f407c1.png)
Figure 5
![[Image: e054cdf7335c583c8817029021f0a0b3.png]](https://gyazo.com/e054cdf7335c583c8817029021f0a0b3.png)
Figure 6
![[Image: 4a7b9cc87e9234ee4d7f48adba2c878a.png]](https://gyazo.com/4a7b9cc87e9234ee4d7f48adba2c878a.png)
Figure 7
![[Image: 1743bbb0e08425dc9563ea50032cc73e.png]](https://gyazo.com/1743bbb0e08425dc9563ea50032cc73e.png)
Figure 8
![[Image: 5844e03fe982a4a7b60fc278aa715d3d.png]](https://gyazo.com/5844e03fe982a4a7b60fc278aa715d3d.png)
Figure 9
Analysing The Results
I paid the most attention to four stats: win percentage, average point differential, individual interceptions per game and individual passes defended per game. The other stats tracked, like pass and rush yards allowed, are interesting and might help to explain the four stats I care about, but they’re not stats to make build decisions using.
Win percentages/point differential
The all-around cornerbacks won 18998/38400 games, for a 49.47% win rate (figure 3). The man-to-man cornerbacks won 18882/38400 games, for a 49.17% win rate (figure 6). The zone cornerbacks won 18718/38400 games, for a 48.74% win rate (figure 9).
When comparing the win percentage of all-around against that of zone, the difference in win percentage was calculated as being significant at a 95% confidence level. This means that we can be 95% confident that the increase in win percentage is a result of the changed variable (the archetype) rather than random chance.
The differences in win rate between all-around and man-to-man and man-to-man and zone were not statistically significant. This suggests that either the sample size was not big enough to capture the differences in win rate, or that there is no significant difference in the win rate.
The average point differential over the 38400 games was -0.11 for all-around (figure 3), -0.02 for man-to-man (figure 6) and -0.33 for zone (figure 9). I’ve pointed this out here because I thought this was noteworthy when viewed compared to the win percentages. It might be tempting to assume that because man-to-man’s win rate is roughly halfway between that of all-around and zone, that man-to-man is the second best archetype by win percentage. The fact that the man-to-man archetype average a better point differential despite losing fewer games further evidences the case that we can’t confidently distinguish between the win rate of all-around and man-to-man.
This suggests that (at least, in these settings) that the all-around archetype wins more games than the zone archetype, but that we cannot confidently draw conclusions from the differences between all-around and man-to-man, and man-to-man and zone.
Individual Stats
This is where we start to see some more exciting conclusions. Over their respective 38,400 games, the all-around cornerbacks intercepted 6947 passes (figure 3), the man-to-man cornerbacks intercepted 6455 passes (figure 6), and the zone cornerbacks intercepted 7663 passes (figure 9). These differences are much bigger than the differences in win rate.
When comparing the number of intercepted passes (7633) of the zone archetype to that of the man-to-man archetype (6455), the difference in interceptions per game was calculated as being significant at a 99% confidence level. This means that we can be 99% confident that the increase in win percentage is a result of the changed archetype rather than random chance. As I’m sure you know, 99% is bigger than 95%. This strongly indicates that zone cornerbacks are better at intercepting passes than man-to-man cornerbacks.
When comparing the zone (7633 interceptions) and all-around (6947 interceptions) cornerbacks, the difference in interceptions per game was calculated as being significant at a 99% confidence level. This is another strong relationship, and gives us good reason to believe that zone cornerbacks accumulate more interceptions per game than both man-to-man cornerbacks, and all-around cornerbacks.
When comparing the number of interceptions of all-around (6947) and man-to-man (6455) cornerbacks, the difference in interceptions per game is also significant at a 99% confidence level. We can be 99% confident that the all-around cornerbacks intercepted more passes than the man-to-man cornerbacks because they are better at intercepting passes rather than because of random chance.
I had intended to do a similar analysis of the number of passes defended, as interceptions are a statistically rarer event, but with the different archetypes accumulating interceptions at ~0.17-~0.2 per game I feel more comfortable with the conclusions above than I expected to. The all-around archetype accumulated 41202 passes defended (figure 3), the man-to-man archetype 41623 (figure 6) and the zone archetype 42529 (figure 9). The fact that the number of interceptions per game and the number of passes defended per game aren’t closely correlated is somewhat surprising to me – I expected the zone archetype to have significantly more than the all-around archetype, and then all-around archetype to have significantly more than the man-to-man archetype. Perhaps the events in the sim that lead to these outcomes aren’t as related as you might think, but given that we don’t see interceptions and passes defended on the 2D viewer that’s all speculation.
So, in terms of individual stats (notably interceptions per game), the evidence suggests that zone > all-around > man-to-man.
Update Scale Observations
It costs far more TPE to raise attributes the higher the attributes are. This is relevant to the study because only the man-to-man archetype has three attributes capped at 100 (agility, speed and endurance) while all-around and zone have only one (endurance) and their other stats are higher to balance them on the field and in terms of TPE required to max out the build.
Maxing out the all-around archetype costs marginally less TPE than the other archetypes (I originally thought the differences in TPE required were more pronounced, but it turns out that the player builder at http://iltornante.com/forum/playerbuilder.html has the incorrect attribute caps and won’t let you calculate the TPE cost of the correct ones). To max out the all-around archetype (excluding the irrelevant stats like throwing, kicking and blocking) you need 1155 TPE, whereas you need 1160 TPE to max out the other two archetypes. The fact the difference is only 5 TPE means that this has no singificant impact on how you should build your player.
Other Observations
If you look more closely at figures 1, 2, 4, 5, 7 and 8 you’ll see that different archetypes performed more or less well against different opponents. These observations are with much smaller sample sizes so I’d be much more hesitant about drawing firm conclusions based on them. We can make some interesting speculative judgements from them, though.
Some teams pass more than they run, and others run more than they pass. I expected to find that the zone archetype, as the least physical of the three with 65 strength and 95 speed, would have a comparatively low win rate against run-heavy teams but the data doesn’t really support this. The zone archetype did fare worse against some run-heavy teams (such as Baltimore and NOLA), but performed better than the other archetypes against some of the other run-heavy teams (such as Honolulu).
They did perform differently against the different conferences, though. The man-to-man archetype had a marginally better win percentage and point differential than the all-around archetype against the NSFC, while games against the ASFC followed the all-around>man-to-man>zone win percentage hierarchy we saw overall. The difference wasn’t particularly big, and the study was conducted against a snapshot of the league. With S23’s rosters, player updates and gameplans the best-performing archetype in each conference could very easily change.
I tested against all twelve teams, but obviously no NSFL player plays all twelve teams in a season (unless they’re traded I suppose). My player plays for Philadelphia, so if I wanted to know what the best performing archetype against the other eleven teams was, I’d subtract Tests 23 and 24 (figures 2, 5 and 8) from the totals found in figures 3, 6 and 9. Ten of our thirteen games are played against our five conference opponents, so I might be more interested in finding out what the best performing archetype against those five teams were. In that case, I’d subtract the tests against Philadelphia from the NSFC totals. The information visible in the tables has all that you need to look into what the best performing archetype against any given selection of teams was if you’re interested in looking what performed best against your specific set of opponents or your conference minus your own team. I probably wouldn’t advise using that information to decide how to build your player, though.
Conclusions
1) The data strongly suggests (with a confidence level of 95%) that the all-around archetype wins more games than the zone archetype in these conditions. The man-to-man archetype probably sits between the two with regards to win percentage but the difference isn’t large enough to say that confidently.
2) The data strongly suggests (with a confidence level of 99%) that the zone archetype intercepts more passes than the all-around archetype, which in turn (with a confidence level of 99%) intercepts more passes than the man-to-man archetype.
3) You need 1155 TPE to max out an all-around cornerback, and 1160 TPE to max out man-to-man and zone. This is a tiny difference which has little bearing on which archetype is best.
4) Different archetypes appear to perform better or worse against different teams, but there are lots of variables in play which make it hard to draw conclusions from this. There isn’t an archetype that clearly performs better or worse against the run or against the pass, so it’s unclear whether a change in meta on the offensive side of the ball would make a particular archetype better or worse.
5) If S22 Week 4 is an accurate representation of the league (to be clear: it may not be), then you should be an all-around cornerback. Unless you hate your team and just want as many interceptions as possible. In which case, you should be a zone cornerback. The differences in win percentage aren’t enormous, and this study isn’t perfect or conclusive. Only you can decide what to do with your player.
Disclaimer: This study is not perfect. I’ll try to explain in as much detail what I did and why for those who are interested, and point out as many potential problems as I can. This is, however, the most comprehensive and actionable study done on this subject.
First, I’d like to thank @mithrandir and @iStegosauruz. mithrandir asked Steg a question on his podcast about studying CB archetypes. This was something I was already interested in studying, and had spoken about with people before. After listening to the podcast, I DM’d Steg and he very kindly gave feedback on my ideas and helped me to determine the methodology I used. mith’s question and subsequent study inspired me to finally do this, and Steg’s feedback helped to make this study far better than it would have been otherwise.
Methodology
I opened a rrecent sim file, the post S22 Week 4 file. I saved three additional versions of it. In the first, I upgraded my player, Brandon Booker, to max attributes for the All-Around archetype and set his height, weight and experience to 6’1, 210 lbs, 12 experience. In the second, I upgraded Booker to a max Man-to-Man corner at 6’3, 205 with 12 experience and in the third I upgraded Booker to a max Zone corner at 6’3, 205 with 12 experience. For tests 23 and 24 of each archetype (the games against Philadelphia) I upgraded Tyler Oles JR of the Arizona Outlaws as like Booker, he’s a rookie outside corner on a team just below .500 playing opposite a higher TPE corner. I don’t honestly know how important those similarities were, but doing that seemed sensible.
I then ran 1600 sims for each archetype, against each team, home and away. I chose 1600 because I can always sim 800 games at a time with no danger of a crash, but simming any more than that introduces a chance that the sim will crash on my laptop. 800 tests often throws up some weird results when I’m testing, so I wasn’t satisfied with 800 game samples. It took minimal additional effort for me to simulate 1600 games rather than, say, 1200 or 1500 so that’s what I did. I would’ve liked to do more so that I’d feel more confident in the results, but doing 1600 in each scenario gives me 115,200. Simulating that many games, exporting the .csv files and copy-and-pasting the relevant information already took long enough.
As Steg brought up during our conversations, a potential pitfall is that I may just be determining what the best build for Brandon Booker would be, or what the best build for an outside CB on the Liberty would be given the other players on the roster, or the best CB build for Week 4. These are valid concerns. This study would be better if I had obtained representative samples against every team using different tempos, playbooks and run:pass ratios and simmed those sets of games multiple times with different test teams and test players. Testing against all the different strats that teams have, might or will use would make this more future-proof, but would also take literally millions of sims. Using a random recent week was the compromise I settled for. Some teams used slower tempos, others faster, some used almost exclusively Spread, others were more balanced or power-heavy. Week 4 will not have perfectly captured the meta, but everything I tested against was a strat that NSFL teams have been using this season.
Finally, I ran significance tests on the results that I thought were the most important indicators using https://abtestguide.com/calc. I’m not a statistician, I took a stats module in university that I don’t remember much of. Please point it out if something looks obviously wrong.
Lots of Tables
Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9Analysing The Results
I paid the most attention to four stats: win percentage, average point differential, individual interceptions per game and individual passes defended per game. The other stats tracked, like pass and rush yards allowed, are interesting and might help to explain the four stats I care about, but they’re not stats to make build decisions using.
Win percentages/point differential
The all-around cornerbacks won 18998/38400 games, for a 49.47% win rate (figure 3). The man-to-man cornerbacks won 18882/38400 games, for a 49.17% win rate (figure 6). The zone cornerbacks won 18718/38400 games, for a 48.74% win rate (figure 9).
When comparing the win percentage of all-around against that of zone, the difference in win percentage was calculated as being significant at a 95% confidence level. This means that we can be 95% confident that the increase in win percentage is a result of the changed variable (the archetype) rather than random chance.
The differences in win rate between all-around and man-to-man and man-to-man and zone were not statistically significant. This suggests that either the sample size was not big enough to capture the differences in win rate, or that there is no significant difference in the win rate.
The average point differential over the 38400 games was -0.11 for all-around (figure 3), -0.02 for man-to-man (figure 6) and -0.33 for zone (figure 9). I’ve pointed this out here because I thought this was noteworthy when viewed compared to the win percentages. It might be tempting to assume that because man-to-man’s win rate is roughly halfway between that of all-around and zone, that man-to-man is the second best archetype by win percentage. The fact that the man-to-man archetype average a better point differential despite losing fewer games further evidences the case that we can’t confidently distinguish between the win rate of all-around and man-to-man.
This suggests that (at least, in these settings) that the all-around archetype wins more games than the zone archetype, but that we cannot confidently draw conclusions from the differences between all-around and man-to-man, and man-to-man and zone.
Individual Stats
This is where we start to see some more exciting conclusions. Over their respective 38,400 games, the all-around cornerbacks intercepted 6947 passes (figure 3), the man-to-man cornerbacks intercepted 6455 passes (figure 6), and the zone cornerbacks intercepted 7663 passes (figure 9). These differences are much bigger than the differences in win rate.
When comparing the number of intercepted passes (7633) of the zone archetype to that of the man-to-man archetype (6455), the difference in interceptions per game was calculated as being significant at a 99% confidence level. This means that we can be 99% confident that the increase in win percentage is a result of the changed archetype rather than random chance. As I’m sure you know, 99% is bigger than 95%. This strongly indicates that zone cornerbacks are better at intercepting passes than man-to-man cornerbacks.
When comparing the zone (7633 interceptions) and all-around (6947 interceptions) cornerbacks, the difference in interceptions per game was calculated as being significant at a 99% confidence level. This is another strong relationship, and gives us good reason to believe that zone cornerbacks accumulate more interceptions per game than both man-to-man cornerbacks, and all-around cornerbacks.
When comparing the number of interceptions of all-around (6947) and man-to-man (6455) cornerbacks, the difference in interceptions per game is also significant at a 99% confidence level. We can be 99% confident that the all-around cornerbacks intercepted more passes than the man-to-man cornerbacks because they are better at intercepting passes rather than because of random chance.
I had intended to do a similar analysis of the number of passes defended, as interceptions are a statistically rarer event, but with the different archetypes accumulating interceptions at ~0.17-~0.2 per game I feel more comfortable with the conclusions above than I expected to. The all-around archetype accumulated 41202 passes defended (figure 3), the man-to-man archetype 41623 (figure 6) and the zone archetype 42529 (figure 9). The fact that the number of interceptions per game and the number of passes defended per game aren’t closely correlated is somewhat surprising to me – I expected the zone archetype to have significantly more than the all-around archetype, and then all-around archetype to have significantly more than the man-to-man archetype. Perhaps the events in the sim that lead to these outcomes aren’t as related as you might think, but given that we don’t see interceptions and passes defended on the 2D viewer that’s all speculation.
So, in terms of individual stats (notably interceptions per game), the evidence suggests that zone > all-around > man-to-man.
Update Scale Observations
It costs far more TPE to raise attributes the higher the attributes are. This is relevant to the study because only the man-to-man archetype has three attributes capped at 100 (agility, speed and endurance) while all-around and zone have only one (endurance) and their other stats are higher to balance them on the field and in terms of TPE required to max out the build.
Maxing out the all-around archetype costs marginally less TPE than the other archetypes (I originally thought the differences in TPE required were more pronounced, but it turns out that the player builder at http://iltornante.com/forum/playerbuilder.html has the incorrect attribute caps and won’t let you calculate the TPE cost of the correct ones). To max out the all-around archetype (excluding the irrelevant stats like throwing, kicking and blocking) you need 1155 TPE, whereas you need 1160 TPE to max out the other two archetypes. The fact the difference is only 5 TPE means that this has no singificant impact on how you should build your player.
Other Observations
If you look more closely at figures 1, 2, 4, 5, 7 and 8 you’ll see that different archetypes performed more or less well against different opponents. These observations are with much smaller sample sizes so I’d be much more hesitant about drawing firm conclusions based on them. We can make some interesting speculative judgements from them, though.
Some teams pass more than they run, and others run more than they pass. I expected to find that the zone archetype, as the least physical of the three with 65 strength and 95 speed, would have a comparatively low win rate against run-heavy teams but the data doesn’t really support this. The zone archetype did fare worse against some run-heavy teams (such as Baltimore and NOLA), but performed better than the other archetypes against some of the other run-heavy teams (such as Honolulu).
They did perform differently against the different conferences, though. The man-to-man archetype had a marginally better win percentage and point differential than the all-around archetype against the NSFC, while games against the ASFC followed the all-around>man-to-man>zone win percentage hierarchy we saw overall. The difference wasn’t particularly big, and the study was conducted against a snapshot of the league. With S23’s rosters, player updates and gameplans the best-performing archetype in each conference could very easily change.
I tested against all twelve teams, but obviously no NSFL player plays all twelve teams in a season (unless they’re traded I suppose). My player plays for Philadelphia, so if I wanted to know what the best performing archetype against the other eleven teams was, I’d subtract Tests 23 and 24 (figures 2, 5 and 8) from the totals found in figures 3, 6 and 9. Ten of our thirteen games are played against our five conference opponents, so I might be more interested in finding out what the best performing archetype against those five teams were. In that case, I’d subtract the tests against Philadelphia from the NSFC totals. The information visible in the tables has all that you need to look into what the best performing archetype against any given selection of teams was if you’re interested in looking what performed best against your specific set of opponents or your conference minus your own team. I probably wouldn’t advise using that information to decide how to build your player, though.
Conclusions
1) The data strongly suggests (with a confidence level of 95%) that the all-around archetype wins more games than the zone archetype in these conditions. The man-to-man archetype probably sits between the two with regards to win percentage but the difference isn’t large enough to say that confidently.
2) The data strongly suggests (with a confidence level of 99%) that the zone archetype intercepts more passes than the all-around archetype, which in turn (with a confidence level of 99%) intercepts more passes than the man-to-man archetype.
3) You need 1155 TPE to max out an all-around cornerback, and 1160 TPE to max out man-to-man and zone. This is a tiny difference which has little bearing on which archetype is best.
4) Different archetypes appear to perform better or worse against different teams, but there are lots of variables in play which make it hard to draw conclusions from this. There isn’t an archetype that clearly performs better or worse against the run or against the pass, so it’s unclear whether a change in meta on the offensive side of the ball would make a particular archetype better or worse.
5) If S22 Week 4 is an accurate representation of the league (to be clear: it may not be), then you should be an all-around cornerback. Unless you hate your team and just want as many interceptions as possible. In which case, you should be a zone cornerback. The differences in win percentage aren’t enormous, and this study isn’t perfect or conclusive. Only you can decide what to do with your player.
![[Image: morisig.png]](https://cdn.discordapp.com/attachments/688507586177532117/1082481732604924036/morisig.png)
[OPTION]S24 (PHI): 16 GP, 73 tackles, 1 TFL, 2 FF, 3 sacks, 5 INTs, 10 PDs, 2 TDs
[OPTION]S25 (PHI): 16 GP, 67 tackles, 4 INTs, 13 PDs, 1 TD
[OPTION]S26 (OCO): 16 GP, 68 tackles, 1 TFL, 1 sack, 2 INTs, 10 PDs
[OPTION]S27 (OCO): 16 GP, 116 tackles, 4 INTs, 23 PDs, 1 TD
[OPTION]S28 (OCO): 16 GP, 84 tackles, 1 FF, 1 FR, 3 INTs, 20 PDs, 1 TD
[OPTION]S29 (OCO): 16 GP, 99 tackles, 3 FF, 1 FR, 5 INTs, 23 PDs, 1 TD
[OPTION]=============================================================
[OPTION]ISFL Playoff Stats:
[OPTION]S23 (PHI): 1 GP, 2 tackles
[OPTION]S26 (OCO): 1 GP, 5 tackles, 2 PDs
[OPTION]=============================================================
[OPTION]Trophies and Achievements:
[OPTION]Drafted 35th Overall by Myrtle Beach in the S21 DSFL Draft
[OPTION]S21 Ultimini Champion
[OPTION]S21 DSFL Pro Bowl Selection
[OPTION]S21 DSFL Defensive Back of the Year Nominee
[OPTION]Drafted 4th Overall by Philadelphia in the S22 ISFL Draft
[OPTION]S23 ISFL Pro Bowl Selection
[OPTION]S23 ISFL Cornerback of the Year Nominee
[OPTION]S23 ISFL Defensive Performance of the Year Nominee
[OPTION]S24 ISFL Pro Bowl Selection
[OPTION]S24 ISFL Cornerback of the Year Nominee
[OPTION]S25 (PHI): 16 GP, 67 tackles, 4 INTs, 13 PDs, 1 TD
[OPTION]S26 (OCO): 16 GP, 68 tackles, 1 TFL, 1 sack, 2 INTs, 10 PDs
[OPTION]S27 (OCO): 16 GP, 116 tackles, 4 INTs, 23 PDs, 1 TD
[OPTION]S28 (OCO): 16 GP, 84 tackles, 1 FF, 1 FR, 3 INTs, 20 PDs, 1 TD
[OPTION]S29 (OCO): 16 GP, 99 tackles, 3 FF, 1 FR, 5 INTs, 23 PDs, 1 TD
[OPTION]=============================================================
[OPTION]ISFL Playoff Stats:
[OPTION]S23 (PHI): 1 GP, 2 tackles
[OPTION]S26 (OCO): 1 GP, 5 tackles, 2 PDs
[OPTION]=============================================================
[OPTION]Trophies and Achievements:
[OPTION]Drafted 35th Overall by Myrtle Beach in the S21 DSFL Draft
[OPTION]S21 Ultimini Champion
[OPTION]S21 DSFL Pro Bowl Selection
[OPTION]S21 DSFL Defensive Back of the Year Nominee
[OPTION]Drafted 4th Overall by Philadelphia in the S22 ISFL Draft
[OPTION]S23 ISFL Pro Bowl Selection
[OPTION]S23 ISFL Cornerback of the Year Nominee
[OPTION]S23 ISFL Defensive Performance of the Year Nominee
[OPTION]S24 ISFL Pro Bowl Selection
[OPTION]S24 ISFL Cornerback of the Year Nominee
[OPTION]S26 ISFL Pro Bowl Selection
[OPTION]S26 ISFL Returner of the Year Nominee
[OPTION]S29 ISFL Pro Bowl Selection
[OPTION]S29 ISFL Cornerback of the Year Nominee
[OPTION]=============================================================
Player | Update | Wiki | Twitter
[OPTION]S26 ISFL Returner of the Year Nominee
[OPTION]S29 ISFL Pro Bowl Selection
[OPTION]S29 ISFL Cornerback of the Year Nominee
[OPTION]=============================================================
Player | Update | Wiki | Twitter
![[Image: 4L0iBsK.png]](https://i.imgur.com/4L0iBsK.png)
![[Image: 54047703d92dd1298abe1ed9af5e3c98.gif]](https://i.gyazo.com/54047703d92dd1298abe1ed9af5e3c98.gif)
![[Image: clemente.gif]](https://sig.grumpybumpers.com/host/clemente.gif)
![[Image: tinytimsbutthole.gif]](https://sig.grumpybumpers.com/host/tinytimsbutthole.gif)
![[Image: image0.png]](https://cdn.discordapp.com/attachments/372602034198806530/707467132568338462/image0.png)
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