Production from last year

[smokey_wasp]

I see us at 2-2 with CPJ. Which is what Collins should be. Temple sees the triple option every year I think we lose that one. I think Tech beats USF this year with CPJ as well, and the Citadel is a win.

I suspect this as well but we will never know.

 

[gthxxxx]

Here's OFEI, which correlates pretty well:
View attachment 6854
Higher is better. In the years we had production come back uninjured, or had a Dedrick Mills, we won a lot. But it looks to me like losing talent and experience hit us hard, or injuries did.
OFEI Ranking is inverted, but shows the same story:
View attachment 6855

I did a quick correlation coefficient calculation of win percent vs ofei based on your first plot and only got a value of 0.6141, which doesn't strike me as particularly strong; absolute correlation coefficient ranges from 0 (weakest) to 1 (strongest). However, I didn't have your exact OFEI values and I eye-balled the values from the plot to the nearest tenth, so correlation result is not as accurate as it could be.
OFEI values I used going from past to present: -0.4, -0.1, 0.9, -0.2, 0.7, 0.8, 0.4, 1.4, -0.3, 0.5, 0.4, 0.6.

 

[gt02]

Temple hard to say CPJ would score more but his defense would give up more.

You really think we wouldn’t have scored more than 2 pts? Lol. Ok whatever. Not even going to waste my time.

 

[stech81]

You really think we wouldn’t have scored more than 2 pts? Lol. Ok whatever. Not even going to waste my time.

Dude read it again I said CPJ would score more but the defense could have give up more

 

[gt02]

Dude read it again I said CPJ would score more but the defense could have give up more

Lol. Ok, withdrawn. In my defense, some punctuation would've helped. I read it as: "hard to say CPJ would have scored more," where I think you are saying "hard to say, CPJ would have scored more."

 

[stech81]

Lol. Ok, withdrawn. In my defense, some punctuation would've helped. I read it as: "hard to say CPJ would have scored more," where I think you are saying "hard to say, CPJ would have scored more."

I graduated from Southern Tech we didn't worry about English .

 

[jchens_GT]

Teams tend to do well when their playmakers come back, and have a hard time replacing that production when they leave or graduate. We lost a lot of production from 2018; an unusual amount.

We didn't pass much, but TaQuon had 82% of our passing yardage, 71% of our passing TDs, and all of our interceptions:

                                                          Pass
Rk            Player  Cmp Att   Pct Yds  Y/A AY/A TD Int  Rate
1    TaQuon Marshall   48 109  44.0 900  8.3  7.5  5   4 121.2
2      Tobias Oliver    7  16  43.8 167 10.4 12.9  2   0 172.7
3       James Graham    1   1 100.0  22 22.0 22.0  0   0 284.8

Provided by CFB at Sports Reference: View Original Table
Generated 10/2/2019.

Rushing, we lost 1642 yards of rushing from TaQuon, Benson, Clinton Lynch, and Qua Searcy, and 18 of our touchdowns. We had 28 touchdowns from other players. We lost 39% of our yards and 39% of our rushing points.
Passing, we lost 57% of our receptions, with Stewart being our biggest receiver, and Lynch and Searcy #4 and #4. We lost 86% of our receiving touchdowns.

                                     Rush            Rece              Scri
Rk                Player Att Yds  Avg  TD Rec Yds  Avg TD Plays Yds  Avg TD
1        TaQuon Marshall 216 971  4.5  11   1  22 22.0  0   217 993  4.6 11
2          Tobias Oliver 152 876  5.8                  12   152 876  5.8 12
3           Jordan Mason 108 659  6.1   7   1   2  2.0  0   109 661  6.1  7
4           Jerry Howard 107 564  5.3   5   4  50 12.5  0   111 614  5.5  5
5          Nate Cottrell  46 362  7.9   4   3  12  4.0  0    49 374  7.6  4
6          Clinton Lynch  41 206  5.0   2   7 209 29.9  2    48 415  8.6  4
7             Qua Searcy  35 349 10.0   3   9 247 27.4  1    44 596 13.5  4
8        KirVonte Benson  15 116  7.7   2   1  -2 -2.0  0    16 114  7.1  2
9           James Graham   5  27  5.4                   0     5  27  5.4  0
10        Joseph Marcina   5  17  3.4                   0     5  17  3.4  0
11        Omahri Jarrett   4  47 11.8                   0     4  47 11.8  0
12      Christian Malloy   4  27  6.8                   0     4  27  6.8  0
13          Xavier Gantt   2   2  1.0                   0     2   2  1.0  0
14            Jalen Camp   1   3  3.0   0  11 186 16.9  0    12 189 15.8  0
15          Melvin Davis   1   3  3.0                   0     1   3  3.0  0
16   Pressley Harvin III   1  -1 -1.0                   0     1  -1 -1.0  0
17          Antwan Owens   1  -2 -2.0                   0     1  -2 -2.0  0
18                           Brad Stewart  15 268 17.9  3    15 268 17.9  3
19                         Malachi Carter   3  56 18.7  1     3  56 18.7  1
20                        Stephen Dolphus   1  39 39.0  0     1  39 39.0  0

Provided by CFB at Sports Reference: View Original Table
Generated 10/2/2019.

We lost at least 40% of our tackles, 65% of our tackles for loss, and 60% of our sacks:

                                            Tack               Def            Fumb
Rk                  Player Solo Ast Tot Loss  Sk Int Yds  Avg TD PD   FR Yds TD FF
1             Malik Rivera   42  26  68  0.5 0.0   2  84 42.0  0                 1
2            Jalen Johnson   38  27  65  6.5                    2.0           1  0
3           Brant Mitchell   35  29  64  5.5                 2.0                 1
4          Tariq Carpenter   35  20  55  1.5 0.0   2   6  3.0  0              5  1
5           Charlie Thomas   29  19  48  3.0                 1.0  1           3  1
6        Anree Saint-Amour   29  18  47 12.0 4.0   2  19  9.5  1              1  3
7              David Curry   28  19  47  0.5 0.5   1   0  0.0  0  0        1  1  0
8               Ajani Kerr   31   7  38  0.0                 0.0  2           1  0
9           Desmond Branch   17  20  37  5.0 0.5   1   2  2.0  0                 1
10          Lamont Simmons   22   7  29  1.0 0.0   1   0  0.0  0                 1
11    Kyle Cerge-Henderson    9  18  27  4.0                    2.0           1  0
12           Jaytlin Askew   17   9  26  1.0                 0.0                 1
13        Victor Alexander   17   8  25  2.0                    1.0           1  1
14            Tre Swilling   18   6  24  1.5 1.0   1   0  0.0  0              6  1
15           Brandon Adams   12  12  24  5.0                    0.0           1  2
16            Kaleb Oliver    9  12  21  2.5 0.0   1  25 25.0  0              2  1
17            Antwan Owens   13   8  21  0.0                    0.0           1  0
18   Bruce Jordan-Swilling   13   7  20  0.0                    0.0           1  0
19      Christian Campbell   11   8  19  2.5                 2.0                 1
20          Juanyeh Thomas   11   4  15  0.0 0.0   1  95 95.0  1  1           1  0
21            Quez Jackson    9   6  15  0.0                                   0.0
22        Jaquan Henderson    8   4  12  0.0                                   0.0
23         Jordan Domineck    4   1   5  0.0                                   0.0
24           T.K. Chimedza    1   3   4  0.0                                   0.0
25     Brentavious Glanton    1   3   4  0.0                                   0.0
26            Chris Martin    2   2   4  0.0                                   0.0
27           Zamari Walton    3   1   4  0.0                 0.0                 3
28             Tre Jackson    2   1   3  0.0                    0.0           1  0
29           Avery Showell    3   0   3  0.0                                   0.0
30             Jarett Cole    1   1   2  0.0                                   0.0
31             Shawn Davis    2   0   2  0.0                                   0.0
32            Quon Griffin    2   0   2  0.0                                   0.0
33          Omahri Jarrett    1   1   2  0.0                                   0.0
34             Jaylon King    2   0   2  0.0                                   0.0
35           Kelton Dawson    0   1   1  0.0                                   0.0
36          Justice Dingle    1   0   1  0.0                                   0.0
37         Bailey Ivemeyer    1   0   1  0.0                                   0.0
38            Jahaziel Lee    1   0   1  0.0                                   0.0
39            Zach Roberts    0   1   1  0.0                                   0.0
40              Qua Searcy    1   0   1  0.0                                   0.0
41             Devin Smith    1   0   1  0.0                                   0.0
42         Dameon Williams    0   1   1  0.0                                   0.0

Provided by CFB at Sports Reference: View Original Table
Generated 10/2/2019.

You have maybe already linked to this, but look who is #119 on this list for overall returning production:

https://www.sbnation.com/college-fo...9-ncaa-football-returning-starters-experience

 

[gthxxxx]

You have maybe already linked to this, but look who is #119 on this list for overall returning production:

https://www.sbnation.com/college-fo...9-ncaa-football-returning-starters-experience

Am I misreading something, or is that link (which I have seen referenced elsewhere on this board, so not just picking on you) claiming correlation coefficients with values such as 0.324, 0.234, 0.168, 0.153, 0.404, 0.377, 0.325, 0.324, 0.299, 0.269, 0.250, 0.250, and 0.228 mean anything? Because the only information gleaned from all those values is that there is no meaningful information gained. Very loosely, a meaningful correlation metric would be something like 0.7+.

 

[jchens_GT]

Am I misreading something, or is that link (which I have seen referenced elsewhere on this board, so not just picking on you) claiming correlation coefficients with values such as 0.324, 0.234, 0.168, 0.153, 0.404, 0.377, 0.325, 0.324, 0.299, 0.269, 0.250, 0.250, and 0.228 mean anything? Because the only information gleaned from all those values is that there is no meaningful information gained. Very loosely, a meaningful correlation metric would be something like 0.7+.

I got a C in stats at GT.

 

[slugboy]

I did a quick correlation coefficient calculation of win percent vs ofei based on your first plot and only got a value of 0.6141, which doesn't strike me as particularly strong; absolute correlation coefficient ranges from 0 (weakest) to 1 (strongest). However, I didn't have your exact OFEI values and I eye-balled the values from the plot to the nearest tenth, so correlation result is not as accurate as it could be.
OFEI values I used going from past to present: -0.4, -0.1, 0.9, -0.2, 0.7, 0.8, 0.4, 1.4, -0.3, 0.5, 0.4, 0.6.

Considering that you’re making a correlation on just 11 data points, and you’re only looking at the absolute (not going from -1 (strong anti) to 0 (not) to 1 (strong correlation), I’d say ~60% correlation is pretty good.


Sent from my iPad using Tapatalk

 

[gthxxxx]

Considering that you’re making a correlation on just 11 data points, and you’re only looking at the absolute (not going from -1 (strong anti) to 0 (not) to 1 (strong correlation), I’d say ~60% correlation is pretty good.


Sent from my iPad using Tapatalk

I use absolute value because that's the only thing that matters for the purpose of discovering patterns, i.e. a correlation coefficient of -x gives the same meaning for our purpose as +x. I personally wouldn't say ~60% correlation is pretty good; I'd evaluate it as inconclusive (i.e. not very meaningful). The few number of data points also indicates low confidence (i.e. wide interval margins), which further indicates such.

Very loosely, a meaningful correlation metric would be something like 0.7+.