What’s Your Magic Number?

The most successful businesses — and certainly, sales departments — have identified their Key Performance Indicators (KPI); individual gateways that directly effect the outcome of a particular process. Then they measure the competency ratios in line with them.

Have you identified the KPIs in your sales process?

A good KPI example in the sales process might be how many times you advance the first sales appointment to the next phase, whether that’s a demonstration, a site visit, a survey or a proposal. Another KPI is how many times you gain a new customer once the first gateway is passed. And when you do gain a new customer, what’s the average revenue you achieve? That’s certainly an important KPI. Because if your average revenue per sale is 40% less than the average peer KPI, you might want to find out why and take focused action to improve it, as you’re leaving money on the table.

And what about the length of a sales cycle in days? Is that conditional or do you have a degree of control over it? If you have a team member that has an average sales cycle 30% shorter than the peer group, uncover and assimilate those best practices out to the rest of the sales team. Less time, more results. That makes ‘Sales Cycle’ a valuable KPI.

On a practical level, KPIs can provide management prospect reactions to their service offering for feedback to marketing and product development, detect problem areas in sales performance and signal the need for strategic or tactical modifications — even an all-out intervention through pinpoint sales performance training.

Perhaps the most overlooked KPI is the individual ‘Magic number’; how many new weekly sales opportunities must be generated based on neighboring KPI’s. Think of the magic number as the fuel in your gas tank needed to get from point A to point B. It’s directly proportional to how far a distance, how fast you drive and your average miles per gallon. Your sales process ‘Magic number’ is a derivative of your average revenue per sale, 1st appointment to proposal ratio, closing ratio and revenue goal. It’s your ‘Activity barometer’ and it should be at 100%.

The following are some tips for improving several sales process KPI’s.

If your current 1st Appointment to Proposal ratio is below 65%:

1. Internally define what your ‘Next step’ objective of the 1st appointment is; a demo, a site visit, a survey or a proposal. Then train to a process and measure the outcome.

2. Decide to start at the ‘Top’ with the fiscal authority that can ‘Call the shots’.

3. Avoid ‘Selling’ your product on the 1st appointment. Instead, outline your diagnostic steps to evaluate the fit between your solutions parallel to their business objectives.

If your current Closing ratio is below 65%:

1. Ask pertinent questions to what the Prospect Company’s decision-making process is, what the internal criteria for change is and what players need to be involved for evaluation.

2. Communicate a timeline and set a specific date for the 2nd appointment before leaving the 1st appointment. Encourage that all management players be present at the next appointment.

3. Catalog risk factors for each management player and develop strategies, tactics, and tools for direct communication to them.

4. Have relevant industry and title reference letters available for ‘Real-time’ credibility.

If your current ‘Activity barometer’ is below 100%:

1. Announce the Competency of converting conversations to appointments as a Key performance Indicator for sales success.

2. Define an appointment setting training objective and set a realistic goal.

3. Develop a training process in line with prospecting scenarios and best practice communications.

4. Don’t sell your ‘Widget’; sell the Business reason to meet.

5. Partner with technology to transfer best prospecting practices into ‘Intellectual capital’ promotion throughout your sales society.

Ultimately, sales trainers and management should work in concert to create a new culture by replacing random sales routines with specific KPI competency training.

Targeted and timely KPI training can make a critical difference to your monthly revenue scorecard. In today’s high sales performance culture migrate away from monthly and quarterly ‘Quota’ focus to daily routines and weekly goals. The opportunity rests squarely on switching paradigms from the required ‘End result’ to the necessary steps (KPIs) to get there routinely. Then build supporting tools for learning and application.

And don’t forget your ‘Magic Number’.

NFL Situation Spotlight #109 – Offensive Holding Penalties (OHP)

Those of you that have had a chance to read some of my past articles may have come across a write-up discussing the predictive power behind Play-book Execution Penalties, which are flags thrown when plays break-down, usually on offense. Penalty calls that fall into this category include infractions such as: Intentional Grounding, Ineligible Receivers, Illegal Shifts and Motions, Too Many Men on the Field, and so on.

PBEP’s are not the only measure of team penalties that have been shown to be a profitable tool for spread handicapping: Offensive Holding calls are also the basis for a situation that has produced big profits over the past 7 years– a situation which has been highly effective even with only one Primary condition involved.

The condition I am speaking about is simple, and involves looking at teams that currently have a higher per-game average for Offensive Holding Penalties Against (OHPA) than their current opponent.

As an example, a team that has played 4 games and been flagged 9 times for Offensive Holding during this stretch, would have an OHPA of 2.25 (9 / 4) and would therefore be subject to this situations logic when facing an opponent with an OHPA average of 2.24 or less.

As you might expect, teams with a higher OHPA have not been a good wager over the past 7 seasons. You might be surprised; however, at just how badly they have fared.

Since 2001, teams with a higher OHPA have been a brutal 518-602 (46.3%) ATS when playing between Week 4 and 15, creating a profit of $3,220.00 at 10/11 odds with $110.00 wagers against the team in question. Not bad for a relatively simple situation with 1 Primary condition (OHPA > OP OHPA) and a ‘Secondary’ stipulation (i.e., ‘tightener’) excluding games very early, and very late in the season.

If there is one thing I have learned through the process of handicapping hundreds of NFL games over the past decade-or-so and studying countless trends during this same time period, it’s that, the stats that are ‘off the beaten track’ are usually the ones that produce the most profitable ‘stand-alone’ trends’–meaning, those that are based one single condition or at least a minimal amount of conditions.

You will be hard-pressed to find another situation based on the more common measurements of team skill, such as rushing and passing stats, that could produce a similar result of +/- 85 wins ATS over a 1000-1100 game stretch, especially when it involves only a single ‘building block’, or, ‘Primary’ condition.

The reason for this is actually fairly simple: Most of us know that Vegas sets the NFL line based predominantly on public perception of team strength. This is a point which even most novice handicappers are aware of these days. Sportsbooks get their 10% ‘Vig’ regardless of who wins and losses and it’s always been in their best interest to set lines that produce balanced action which helps to minimize their immediate risk and maximize long-term profits.

With the knowledge that the point spread is more a product of public sentiment, than actual team skill levels in many cases, it becomes fairly safe to assume that the statistics that help to shape public opinion will probably be less effective at handicapping the spread than other, equally effective stats that perhaps ‘fly-below the radar’ of the vast majority of handicappers out there. Those who follow the stock market will be familiar with this concept, which is known as the efficient market theory.

As an example: if everyone made their wagers based solely on season-to-date points differential for each team, Vegas would correct their lines for this fact and using a method of choosing teams based on points scored alone, would ultimately yield a fat 0 dollars profit, if not a loss, over the long-haul.

This example is an over-simplification of course, and bettors will typically take many more things into consideration when making wagers. Having said that, there are certain stats and variables that are used more often-than-not by the average handicapper, week in and week out.

With-out a doubt, rushing and passing stats are the measures of choice for most novice-to-intermediate handicappers along with other obvious ones, such as, points scored and allowed; ‘power’ numbers; injury report data and recent head-to-head results. Most people base their wagering decisions on these kinds of stats because they are both easy to find and easy to understand.

As with the financial markets; however, following the ‘herd’ is more likely to lead you (and your bankroll) over the side of a cliff, rather than to the ‘pot of gold’, and the same rules apply when handicapping the sports-betting market.

This is not to say that basic statistics which focus on such things as the efficiency of a team’s rushing and passing game are to be ignored. On the contrary, I use these fundamental measurements (expressed as yards-per-play differentials) as part of a number of my successful situations. But, a number of other conditions usually need to be added in order to make them truly effective in predicting spread winners.

Getting back to penalties for a moment–beyond the basic penalty yardage totals shown for each team in the final boxscore, the specific types and frequency of certain penalties that teams take are essentially ignored by 99.5% of handicappers, and for the reasons discussed above, these key stats will also not factor too much into the line as a result.

Penalty calls are not the only facet of NFL team play that suffers from a lack of attention, despite their ability to reveal profitable situations versus the spread.

There happens to be quite a few other statistical gems that also fall into the ‘overlooked’ category and one such area concerns special teams play and more specifically, the king of this category–KRYF, which stands for Kick-off Return Yardage (Average) For.

KRYF is a critical stat that is on my ‘shortlist’ of numbers that no good NFL handicapper should be with-out.

It acts as a barometer of overall special teams strength on the most important special teams play of all: the Kick-off return.

Kick-offs are a critical event because of their ability to switch a games momentum in a heart-beat and they provide an opportunity for a team to quickly gobble up crucial yardage that can leave them with decent field position, which is key to any chance of a victory, whether it be SU or ATS.

Nothing deflates a team that just finished putting points on the board more, than an opponent who runs back the ensuing kick-off for 40 yards and we all know the affect that a player like Chicago’s kick-return specialist, Devin Hester, can have on a game’s outcome in the blink of an eye.

The league average for KRYF is usually around 22 yards-per-return. Good teams will find themselves with an average near 25 while lousy return teams will be down near 19 yards-per-return.

KYRF is a stat that I use a lot, and it just happens to be the basis for one of the 2 remaining Primary conditions yet to be discussed. Including the original one involving OHPA, this powerful ‘trifecta’ of negative factors spells doom for the team unlucky enough to meet all of the criteria involved.

Here is how KRYF factors into things: I have found that teams that have a higher OHPA as well as a lower KRYF than their current opponent, have been a dismal 245-332 (42.5%) ATS since 2001, which almost doubles the profit produced from looking at OHPA alone, to $6250.00.

As with OHPA, it makes sense that teams at a disadvantage with regards to KRYF are a poor bet against the spread. The surprise here, once again, is just how profitable it has been historically, when betting against this team based on these 2 simple factors alone.

Now, we are not done quite yet. The final significant stipulation that I like to add also involves special teams, in this case– a comparison of Gross Punt Yardage and Net Punt Yardage concerning the current opponent of the team in question is included.

Subtracting Net Punt Yardage (the yardage achieved by a punt after the return is factored in along with any penalties against the punting team) from Gross Punt Yards (the distance a punt actually traveled from where the ball was snapped) is an excellent way to look at the ability of a team to: A) Execute a punt properly, and B) efficiently cover the ensuing return.

Teams with a poor punt coverage unit or that take a higher-than-average number of penalties during the punt itself; will see a wider gap between their GPYF and NPYF. Teams that have a below-average punter will also have a lower NPYF by extension, as shorter punts do carry a higher risk of big returns if coverage personnel do not have enough time to get into proper position.

The average gap between a team’s GPYF and their NPYF happens to be 6 yards.
By excluding opponents that have a GPYF at least 7 yards higher than their NPYF, we effectively remove opponents that have either poor coverage skills on punts, or a weak punter. Ultimately, this is yet another blow against the team already stinging from the other factors previously discussed.

In summary then: Teams that have a higher per-game average for Offensive Holding Penalties Against (OHPA) along with a lower per-game average for Kick-off Return Yardage For (KRYF)–both in relation to their current opponent–are 142-244 (36.8%) ATS since 2001, so long as this opponent’s Gross Punt Yardage figure is no more than 7 yards bigger than their Net Punt Yardage per-game average.

Based on these 3 Primary conditions (along with the earlier tightener that confines things to Week 4 through 15), we have a trend that has been a consistent winner since ’01 and has produced a profit of $8,780.00 at 10/11 odds during this time period.

Rounding things out, are 2 final limitations, one of which excludes teams who have faced a tough schedule season-to-date (SOS > 0.600) while the other excludes underdogs of >= 7 points. With the addition of these final 2 conditions, the record is reduced to 89-190 (31.9%) ATS–a killer situation that has been a deadly predictor of results ATS, 7 years running.

A brief look at the stats below will show that this is a very balanced trend that has played on every single team in the league, aside from one. And, it is split fairly evenly between favs and dogs as well as home and away teams.

Here are all the details.

(Notes: ASMR stands for Average Spread Margin Rating. A positive rating indicates a trend that is stronger than average versus the line, negative–weaker than average. TDIS% is the percentage of teams in the league that have been involved in this situation at one time or another. WT% is the percentage of teams that are .500 or better and SPR is the average spread for teams in this situation. For more details, please consult Page 13 of my 2007 NFL Game Sheets Guide.)

Situational Trend #109 Summary

Primary Conditions (Building Blocks)
1) Offensive Holding Penalty Average Against (OHPA) > Opponent.
2) Kick-off Return Yardage Average For (KRYF) Secondary Conditions (Tighteners)
1) Game is between Week 4 and 15.
2) Team is not an Underdog of >=7 Points.
3) Strength of Schedule (SOS), season-to-date, is Situation Stats
ASMR: -0.5
Home%: 57.4
Dog%: 44.9
TDIS%: 96.9
WT%: 54.7
SPR: -1.0
Top Teams: TB(18); BUF(16); MIN(16); MIA(15)

Situation Record
Overall (Since ’01): 89-190 ATS
2007 Season: 10-26 ATS
2006 Season: 9-19 ATS
2005 Season: 11-32 ATS
2004 Season: 17-32 ATS

Last 3 Results. Pick in Brackets.
2007 WK15–TEN 26 KC 17 (TEN -3.5) W
2007 WK15–JAC 29 PIT 22 (JAC +3.5) W
2007 WK15–CLE 8 BUF 0 (CLE -6) W

NFL Situation Spotlight #76 – Teams with a Big Pass Yardage % For (BPY%F) > 50%

When an NFL team takes the field on offense, their goal is simple: gain enough yards on each play as to set up an eventual 1st down, thereby moving the chains and starting the whole process over again, until either a field-goal, or preferably a touch-down, is put up on the scoreboard.

First-downs can be achieved in many different ways of course; either through the air, or on the ground; via the big-play, or by using a more conservative approach that involves more short-yardage conversions in 3rd-down situations.

Regardless of whether a team is built around speedy Pro-Bowl receivers that shred an opponents defense for long gains or they take a more traditional route, involving up-the-middle ‘smash-mouth’ runs with a mix of short-yardage pass attempts thrown in for good measure–all coaching staffs will use the players they have on the field and their accompanying skill sets in the best possible manner to get that next first down, or score.

The important question for those of us looking to beat the Vegas Point spread is: are there certain styles of offense that in the right situations, cover the spread at a higher rate than others?

The answer is yes and this article will briefly explore one style of offense that has produced some very good results against the spread over the past 7 years when a certain statistical bench-mark is achieved.

The particular style of offense I am talking about involves teams that produce a high percentage of big pass play yardage as part of their overall yardage gained by throwing the football.

My official label for this stat is BPY%F (Big Pass Yardage Percentage For) and it is a measurement of the percentage of total team passing yards that were gained from passing plays of 20 or more yards.

Dallas led the league in this category in 2007. 42.5% of the Cowboys passing yardage for the season came on plays of >= 20 yards. Green Bay and San Diego rounded out the top 3. The league average for BPY%F has typically been around 40% in most years over the past decade, but this fell to 37.5% in 2007.

It was actually a good year versus the spread for teams that rely on the deep ball: The top 8 teams in the league for BPY%F were a combined 75-45 ATS and none of the 8 had an ATS record under .500. Conversely, the bottom 8, led by Baltimore’s brutal pass attack (they had a BPY%F of only 25.5%) were a dismal 50-74 ATS.

These interesting results have not played out in a consistent manner over the past 7 years; however, and in some years, teams with a high BPY%F have only been mediocre against the number overall while those at the bottom end of the scale have been closer to .500 ATS.

When we look at teams entering a game with an extremely high BPY%F (greater than 50%); though, a consistent pattern does begin to emerge.

Since 2001–which is when I began to track BPY–teams have been an excellent 145-119 (54.9%) ATS when entering a game with a BPY%F of greater than 50% on the season.

Teams that have this large a percentage of big pass play yards are normally only seen in the first 6-7 weeks of the season, before a mounting number of pass attempts begin to reduce BPY%F to a more normal level, league wide. That’s not to say that some teams have not carried a 50% level all the way to season’s end (Philadelphia from 2006 is a good example, they had a BPY%F well over 50 at the end of that season) only that, this situation does predominantly play on teams that are extremely efficient with the deep-pass right out of the gate.

What we have here is good so far, but, there is one more primary condition that needs to be added to this situation before things really begin to take shape and it involves how ‘game-ready’ the opponent of our focus team happens to be, at this early stage of the season.

Here is the meat of this situation: I have found that teams with a Big Pass Yardage Percentage > 50%, playing a team with a Play Book Execution Penalty per-game average against (PBEPA) of 1.3 or greater are a very strong 56-20 (73.7%) ATS since 2001, for a profit of $3,400.00 when wagering $110 to win back $100.

What are Play Book Execution penalties you might be ready to ask? For those who have not read my NFL Game Sheets Guide, I categorize penalties under a total of 6 different headings and this particular category involves calls such as: Illegal Procedures, Formations, Shifts, Motion, Participation, Snaps and Substitutions; Intentional Grounding; Delay of Game; 12 Men on the Field; Ineligible Receivers, and so on–essentially those flags generated by the break-down of play-calls, mostly on offense. The league average for PBEP’s is normally around 0.7 calls per game (on each team).

It’s a category of penalties that act as a good yardstick for measuring the quality of a team’s coaching staff and also provides an indication if players are being used in schemes where they are comfortable and have the necessary skills to succeed.

Combining a team that is having great success with the deep ball early in the season, with a team that is perhaps at the other end of the spectrum in regards to ‘preparedness’ and offensive efficiency and creativity, creates line value that the astute bettor can exploit.

In addition to the main conditions described above, there are a few secondary conditions that serve to tighten the record of this trend.

Firstly, any games with an Over/Under of greater than 48 are excluded and our focus team must also be coming off a game in which their Time of Possession was 23 minutes or greater (TOPF is an excellent barometer of the overall health of a team, both on offense and defense).

In addition, teams that are coming off back-to-back SU wins of >= 14 points are also excluded as they are more likely to be either overvalued, or at risk for a let-down in the current game.

Lastly, teams that met their current opponent either earlier in the season, or anytime within the previous 2 seasons, and had a turn-over differential (TOD) of Primary Conditions (Building Blocks)

1) Big Pass Yardage % For (BPY%F) > 50%.

2) Opponent’s Play Book Execution Penalty Average Against (PBEPA) > 1.3.

Secondary Conditions (Tighteners)

1) Exclude Over/Under (OU) >= 48.

2) Exclude Time of Possession For (TOPF) in Last Game of = 14 points in Last 2 Games.

4) Exclude Turn-over Differential (TOD) Situation Stats

ASMR: +0.8

Home%: 55.4

Dog%: 42.9

TDIS%: 65.6

WT%: 75.0

SPR: -0.40

Top Teams: PIT(7); ATL(6); CAR(4); CLE(5)

Situation Records

Overall (Since ’01): 48-6 ATS

2007 Season: 6-1 ATS

2006 Season: 9-0 ATS

2005 Season: 15-1 ATS

2004 Season: 11-1 ATS

Last 3 Results. Pick in Brackets.

2007 WK6–CLE 41 MIA 31 (CLE -4.5) W

2007 WK5–WAS 34 DET 3 (WAS -3.5) W

2007 WK4–IND 38 DEN 20 (IND -9.5) W